TCC
Leonardo Fernandes Wink
08/10/2022
1 Brief explanation
Every boxplot means a monitoring point (Ponto de monitoramento (or PM) in portuguese). My goal here is to analyze the evolution between decades of each water quality parameter that compounds the Water Quality Index (WQI).
The river flows in the east-west direction as shown in the image below.
The logic behind the sorting in the boxplots is because of 2 main reasons:
- The original monitoring point isn’t easy to understand (8 digits, like 87409900)
- Changing the original nomenclature to PM1, PM2 (…) makes it easier to understand that the last point has water contributions of every other point upstream.
Some features that I want to add: - If the parameter is x, then use x’s classes (with its own classes background color plotted) - Define the timescale, should act just like a filter
# plan_wide_19902020 %>%
# filter(ANO_COLETA > "1990" &
# ANO_COLETA <= "2000")2 Anotações de coisas por fazer:
- Descobrir como colocar as estações no sentido correto montante -> jusante nos sumários
87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900
Aprender a segmentar o meu dataset por períodos- aprender a criar uma nova coluna com a segmentação dos períodos
- maybe use
~facet.grid - aprender a colocar a legenda dentro do gráfico
- reduzir o tamanho da legenda
corrigir os valores 0 de IQA pra NA- descobrir como conseguir a equação do lm
aprender a pivotar o sumário-> meu sumário do google docs ta batendo direitinho com o do R- descobrir se há outros TCCs com disponibilização de códigos
Namontá com com casa decimal","eptottá com"."- correlação forte entre condutividade e Namon/Ptot/DBO
| 1990-2000 | 2000-2010 | 2010-2020 |
|---|---|---|
| 1990-2000 | 2000-2010 | 2010-2020 |
3 Instalar os pacotes
# install.packages(tidyverse)3.1 acessar os pacotes
# library(readr)
# library(rmarkdown)
# # library(qboxplot)
# library(readxl)
# library(pillar)
# library(dplyr)
# library(tidyverse)
# library(gapminder)
# library(knitr)
# library(kableExtra)
# library(ggpubr)
# library(gridExtra)
# library(modelsummary)
# library(gtsummary)
# library(GGally)
pacman::p_load(readr, rmarkdown, readxl,
pillar, dplyr, tidyverse,
gapminder, knitr, kableExtra,
gridExtra, #modelsummary,
gtsummary, ggplot2,
ggbeeswarm, GGally,
report)
# pacman::p_load(tibbletime)
cite_packages()## - Allaire J, Xie Y, McPherson J, Luraschi J, Ushey K, Atkins A, WickhamH, Cheng J, Chang W, Iannone R (2022). _rmarkdown: Dynamic Documentsfor R_. R package version 2.14, <URL:https://github.com/rstudio/rmarkdown>.Xie Y, Allaire J, Grolemund G (2018). _R Markdown: The DefinitiveGuide_. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9781138359338,<URL: https://bookdown.org/yihui/rmarkdown>.Xie Y, Dervieux C, Riederer E (2020). _R Markdown Cookbook_. Chapmanand Hall/CRC, Boca Raton, Florida. ISBN 9780367563837, <URL:https://bookdown.org/yihui/rmarkdown-cookbook>.
## - Auguie B (2017). _gridExtra: Miscellaneous Functions for "Grid"Graphics_. R package version 2.3, <URL:https://CRAN.R-project.org/package=gridExtra>.
## - Bryan J (2017). _gapminder: Data from Gapminder_. R package version0.3.0, <URL: https://CRAN.R-project.org/package=gapminder>.
## - Clarke E, Sherrill-Mix S (2017). _ggbeeswarm: Categorical Scatter(Violin Point) Plots_. R package version 0.6.0, <URL:https://CRAN.R-project.org/package=ggbeeswarm>.
## - Henry L, Wickham H (2020). _purrr: Functional Programming Tools_. Rpackage version 0.3.4, <URL: https://CRAN.R-project.org/package=purrr>.
## - Makowski D, Ben-Shachar M, Patil I, Lüdecke D (2021). "AutomatedResults Reporting as a Practical Tool to Improve Reproducibility andMethodological Best Practices Adoption." _CRAN_. <URL:https://github.com/easystats/report>.
## - Müller K, Wickham H (2021). _tibble: Simple Data Frames_. R packageversion 3.1.1, <URL: https://CRAN.R-project.org/package=tibble>.
## - Müller K, Wickham H (2022). _pillar: Coloured Formatting for Columns_.R package version 1.8.0, <URL:https://CRAN.R-project.org/package=pillar>.
## - R Core Team (2020). _R: A Language and Environment for StatisticalComputing_. R Foundation for Statistical Computing, Vienna, Austria.<URL: https://www.R-project.org/>.
## - Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E,Elberg A, Crowley J (2021). _GGally: Extension to 'ggplot2'_. R packageversion 2.1.2, <URL: https://CRAN.R-project.org/package=GGally>.
## - Sjoberg D, Curry M, Hannum M, Larmarange J, Whiting K, Zabor E (2021)._gtsummary: Presentation-Ready Data Summary and Analytic ResultTables_. R package version 1.4.0, <URL:https://CRAN.R-project.org/package=gtsummary>.
## - Wickham H (2016). _ggplot2: Elegant Graphics for Data Analysis_.Springer-Verlag New York. ISBN 978-3-319-24277-4, <URL:https://ggplot2.tidyverse.org>.
## - Wickham H (2019). _stringr: Simple, Consistent Wrappers for CommonString Operations_. R package version 1.4.0, <URL:https://CRAN.R-project.org/package=stringr>.
## - Wickham H (2021). _forcats: Tools for Working with CategoricalVariables (Factors)_. R package version 0.5.1, <URL:https://CRAN.R-project.org/package=forcats>.
## - Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R,Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E,Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, TakahashiK, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to thetidyverse." _Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 (URL: https://doi.org/10.21105/joss.01686).
## - Wickham H, Bryan J (2019). _readxl: Read Excel Files_. R packageversion 1.3.1, <URL: https://CRAN.R-project.org/package=readxl>.
## - Wickham H, François R, Henry L, Müller K (2022). _dplyr: A Grammar ofData Manipulation_. R package version 1.0.9, <URL:https://CRAN.R-project.org/package=dplyr>.
## - Wickham H, Girlich M (2022). _tidyr: Tidy Messy Data_. R packageversion 1.2.0, <URL: https://CRAN.R-project.org/package=tidyr>.
## - Wickham H, Hester J, Bryan J (2022). _readr: Read Rectangular TextData_. R package version 2.1.2, <URL:https://CRAN.R-project.org/package=readr>.
## - Xie Y (2022). _knitr: A General-Purpose Package for Dynamic ReportGeneration in R_. R package version 1.39, <URL:https://yihui.org/knitr/>.Xie Y (2015). _Dynamic Documents with R and knitr_, 2nd edition.Chapman and Hall/CRC, Boca Raton, Florida. ISBN 978-1498716963, <URL:https://yihui.org/knitr/>.Xie Y (2014). "knitr: A Comprehensive Tool for Reproducible Research inR." In Stodden V, Leisch F, Peng RD (eds.), _Implementing ReproducibleComputational Research_. Chapman and Hall/CRC. ISBN 978-1466561595,<URL: http://www.crcpress.com/product/isbn/9781466561595>.
## - Zhu H (2021). _kableExtra: Construct Complex Table with 'kable' andPipe Syntax_. R package version 1.3.4, <URL:https://CRAN.R-project.org/package=kableExtra>.knitr::knit_hooks$set(time_it = local({
now <- NULL
function(before, options) {
if (before) {
# record the current time before each chunk
now <<- Sys.time()
} else {
# calculate the time difference after a chunk
res <- difftime(Sys.time(), now)
# return a character string to show the time
paste("Time for this code chunk to run:", res)
}
}
}))
knitr::opts_chunk$set(time_it = TRUE)3.1.1 referenciando os pacotes
version$version.string## [1] "R version 3.6.3 (2020-02-29)"citation(package = "tidyverse")##
## Wickham et al., (2019). Welcome to the tidyverse. Journal of Open
## Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {Welcome to the {tidyverse}},
## author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
## year = {2019},
## journal = {Journal of Open Source Software},
## volume = {4},
## number = {43},
## pages = {1686},
## doi = {10.21105/joss.01686},
## }Time for this code chunk to run: 0.0210690498352051
3.2 importando a planilha
## Rows: 1,179
## Columns: 53
## $ CODIGO <chr> "87398950", "87398900", "87405500", "87398950", "8~
## $ MUNICIPIO <chr> "GRAVATAI - RS", "GRAVATAI - RS", "CACHOEIRINHA - ~
## $ ENDERECO <chr> "A PE NO TREVO, NA PONTE VELHA OU DE BARCO VINDO D~
## $ COORD_GEO_LAT_GRAU <dbl> -51.00064, -50.93414, -51.11733, -51.00064, -50.93~
## $ COORD_GEO_LONG_GRAU <dbl> -29.95055, -29.95075, -29.95055, -29.95055, -29.95~
## $ Altitude <dbl> 7, 6, 6, 7, 6, 7, 9, 9, 6, 6, 6, 6, 7, 6, 7, 6, 13~
## $ RECURSO_HIDRICO <chr> "RIO GRAVATAI", "RIO GRAVATAI", "RIO GRAVATAI", "R~
## $ DATA_COLETA <date> 1994-12-08, 1994-02-03, 1994-02-03, 1993-10-06, 1~
## $ ANO_COLETA <dbl> 1994, 1994, 1994, 1993, 1994, 1995, 1994, 1995, 19~
## $ Alcalinidade <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ Condutividade <dbl> 90.0, 47.0, 147.0, 43.0, NA, 47.0, 70.0, 60.6, 72.~
## $ DBO <dbl> 5, 5, 11, 3, 3, 2, 4, 3, 4, 2, 5, 3, 1, 1, 5, 2, 2~
## $ IQA_DBO <dbl> 53.97, 53.97, 29.03, 69.06, 69.06, 78.12, 61.05, 6~
## $ DQO <dbl> 26, 47, 34, 37, 54, 23, 35, 25, 28, 40, 21, 35, 21~
## $ E_coli <dbl> 4.0, 40.0, 12.8, 10.4, 32.0, 16.8, 40.0, 6.4, 10.4~
## $ fosfato_orto <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ fosforo_total <dbl> 0.1130, 0.0883, 0.3530, 0.0908, 0.1180, 0.0326, 0.~
## $ IQA_Ptot <dbl> 72.73, 77.95, 38.26, 77.41, 71.71, 91.16, 63.37, 6~
## $ Nitrato <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ Nitrito <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ nitro_organico <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ nitro_amon <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ nitro_kjeldahl <dbl> 1.42, 1.61, 6.62, 0.79, 1.45, 0.76, 1.23, 1.37, 2.~
## $ nitro_total <dbl> 1.42, 1.61, 6.62, 0.79, 1.45, 0.76, 1.23, 1.37, 2.~
## $ IQA_NitroTot <dbl> 89.02, 87.64, 59.33, 93.74, 88.80, 93.97, 90.41, 8~
## $ oxigenio_dissolvido <dbl> 6.5, 8.7, 5.3, 7.8, 7.6, 8.5, 7.3, 6.9, 6.7, 6.3, ~
## $ sat_OD <dbl> 78.162, 116.280, 70.837, 85.270, 96.442, 85.565, 7~
## $ IQA_OD <dbl> 84.58, 90.92, 76.25, 89.67, 97.53, 89.88, 79.57, 7~
## $ pH <dbl> 6.9, 6.8, 6.6, 6.4, 6.9, 6.7, 6.6, 6.7, 6.3, 7.1, ~
## $ IQA_pH <dbl> 89.76, 87.72, 82.86, 76.96, 89.76, 85.42, 82.86, 8~
## $ SDT <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ SST <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ temp_agua <dbl> 25.0, 31.0, 31.0, 20.0, 28.0, 16.0, 16.0, 16.0, 18~
## $ temp_ar <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ transparencia_agua <dbl> NA, 40.0, 30.0, NA, NA, NA, 50.0, 20.0, 40.0, 20.0~
## $ turbidez <dbl> 20.0, 20.0, 8.5, 23.0, 19.0, 17.0, 12.0, 22.0, 17.~
## $ IQA_Turb <dbl> 61.87, 61.87, 80.20, 58.73, 63.07, 65.69, 73.57, 5~
## $ vazao_rio <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ Vazao <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ coliformes_termo <dbl> 5, 50, 16, 13, 40, 21, 50, 8, 13, 23, 700, 16, 8, ~
## $ IQA_coli <dbl> 76.58, 47.21, 60.44, 63.04, 49.66, 57.12, 47.21, 6~
## $ solidos_totais <dbl> 121, 112, 160, 93, 127, 75, 80, 201, 84, 123, 78, ~
## $ IQA_Sól_Tot <dbl> 83.91, 84.69, 79.40, 85.78, 83.28, 86.08, 86.07, 7~
## $ `IQA_^OD` <dbl> 2.126, 2.153, 2.089, 2.148, 2.178, 2.148, 2.104, 2~
## $ `IQA_^temp_agua` <dbl> 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.~
## $ `IQA_^coli` <dbl> 1.917, 1.783, 1.850, 1.862, 1.796, 1.835, 1.783, 1~
## $ `IQA_^pH` <dbl> 1.715, 1.711, 1.699, 1.684, 1.715, 1.705, 1.699, 1~
## $ `IQA_^DBO` <dbl> 1.175, 1.175, 1.271, 1.116, 1.116, 1.072, 1.149, 1~
## $ `IQA_^NitroTot` <dbl> 1.567, 1.564, 1.504, 1.575, 1.566, 1.575, 1.569, 1~
## $ `IQA_^Ptot` <dbl> 1.535, 1.546, 1.440, 1.545, 1.533, 1.570, 1.514, 1~
## $ `IQA_^Turb` <dbl> 1.391, 1.391, 1.420, 1.385, 1.393, 1.398, 1.410, 1~
## $ `IQA_^Sól_Tot` <dbl> 1.425, 1.426, 1.419, 1.428, 1.424, 1.428, 1.428, 1~
## $ IQA <dbl> 61.876, 58.452, 57.556, 57.129, 56.405, 56.189, 55~Time for this code chunk to run: 0.951486110687256
Time for this code chunk to run: 0.176084995269775
4 data wrangling
# Como há dados faltantes, no cálculo entre o produto das colunas, ele acaba interpretando como se fosse zero, mas na verdade é NA
plan_wide_19902020 <- plan_wide_19902020 %>%
mutate(IQA = ifelse(IQA == 0, NA, IQA))
parametros_IQA <- plan_wide_19902020 %>%
select(CODIGO,
pH,
DBO,
E_coli,
nitro_amon,
nitro_kjeldahl,
nitro_total,
fosforo_total,
temp_agua,
turbidez,
solidos_totais,
oxigenio_dissolvido,
Condutividade,
ANO_COLETA)
write.csv(parametros_IQA,
"./parametros_IQA.csv",
row.names = FALSE)
plan_wide_19902020 %>%
select(starts_with("IQA_^")) %>%
mutate(
TESTANDOIQA = prod()
)## # A tibble: 1,179 x 10
## `IQA_^OD` IQA_^temp~1 IQA_^~2 IQA_^~3 IQA_^~4 IQA_^~5 IQA_^~6 IQA_^~7 IQA_^~8
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2.13 1.58 1.92 1.72 1.18 1.57 1.54 1.39 1.42
## 2 2.15 1.58 1.78 1.71 1.18 1.56 1.55 1.39 1.43
## 3 2.09 1.58 1.85 1.70 1.27 1.50 1.44 1.42 1.42
## 4 2.15 1.58 1.86 1.68 1.12 1.58 1.54 1.38 1.43
## 5 2.18 1.58 1.80 1.72 1.12 1.57 1.53 1.39 1.42
## 6 2.15 1.58 1.84 1.70 1.07 1.58 1.57 1.40 1.43
## 7 2.10 1.58 1.78 1.70 1.15 1.57 1.51 1.41 1.43
## 8 2.08 1.58 1.89 1.70 1.12 1.57 1.52 1.39 1.41
## 9 2.09 1.58 1.86 1.68 1.15 1.56 1.49 1.40 1.43
## 10 2.13 1.58 1.83 1.72 1.07 1.57 1.55 1.38 1.42
## # ... with 1,169 more rows, 1 more variable: TESTANDOIQA <dbl>, and abbreviated
## # variable names 1: `IQA_^temp_agua`, 2: `IQA_^coli`, 3: `IQA_^pH`,
## # 4: `IQA_^DBO`, 5: `IQA_^NitroTot`, 6: `IQA_^Ptot`, 7: `IQA_^Turb`,
## # 8: `IQA_^Sól_Tot`
## # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names# library(performance)
# modelo <- plan_wide_19902020 %>%
# select(CODIGO, oxigenio_dissolvido, periodo) %>%
# group_by(CODIGO, periodo) %>%
# lm() %>%
# performance::check_distribution()
# # lm()
#
# check_model(modelo)
# performance::check_autocorrelation(modelo)Time for this code chunk to run: 0.150002002716064
Time for this code chunk to run: 0.00301098823547363
Time for this code chunk to run: 0.00401186943054199
5 setting theme
theme_grafs <- function(bg = "white",
coloracao_letra = "black") {
theme(
plot.title =
element_text(
hjust = 0.5,
color = coloracao_letra,
size = 19),
axis.title.x =
# element_text(
# color = coloracao_letra,
# size = 15,
# angle = 0,),
element_blank(),
axis.title.y = element_text(
color = coloracao_letra,
size = 15,
angle = 90),
axis.text.x = element_text(
color = coloracao_letra,
size = 17),
axis.text.y = element_text(
color = coloracao_letra,
size = 17,
angle = 0),
strip.background = element_rect(fill = bg,
linetype = 1,
size = 0.5,
color = "black"),
strip.text = element_text(size = 17),
panel.background = element_rect(fill = bg),
plot.background = element_rect(fill = bg),
plot.margin = margin(l = 5, r = 10,
b = 5, t = 5)
)
}Time for this code chunk to run: 0.00501418113708496
6 setting different timescales
Time for this code chunk to run: 0.00701999664306641
7 setting sumaries
Time for this code chunk to run: 0.0140459537506104
8 Parâmetros físico-químicos
8.0.1 Oxigênio Dissolvido
Time for this code
chunk to run: 1.94153308868408
Time for this code
chunk to run: 0.770891904830933
Time
for this code chunk to run: 0.643067836761475
Time
for this code chunk to run: 0.613552808761597
grid.arrange(od_p1, od_p2, od_p3, ncol = 3)
Time for this code
chunk to run: 1.94008111953735
ggsave("od.png",
units = c("px"),
width = 4500,
height = 2993,
plot = od,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("od_p1.png",
plot = od_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("od_p2.png",
plot = od_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("od_p3.png",
plot = od_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("od_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(od_p1, od_p2, od_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time for
this code chunk to run: 7.66404604911804
Time for this code chunk to run: 0.00601601600646973
Time
for this code chunk to run: 0.679269075393677
Time
for this code chunk to run: 0.67055606842041
Time
for this code chunk to run: 0.78303599357605
grid.arrange(iqaod_p1, iqaod_p2, iqaod_p3, ncol = 3)
Time
for this code chunk to run: 1.68981695175171
## # A tibble: 7 x 8
## par PM1 PM2 PM3 PM4 PM5 PM6 PM7
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 max 10.8 10.5 10.3 12.1 19.9 10.2 11.1
## 2 q3 7.3 8 7.1 8.2 6 5 5.65
## 3 median 6.4 6.9 5.95 6.3 4.2 2.6 2.9
## 4 mean 5.99 6.78 5.98 7.01 4.22 2.98 3.60
## 5 q1 4.9 5.6 4.4 6 1.9 0.25 1.4
## 6 min 0.8 2 2.5 4.2 0.1 0.1 0.1
## 7 n 101 101 68 30 97 32 65## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.4 3.5 4.9 5.01 6.65 10.9
## 2 87398900 1.9 4 5.5 5.33 6.6 12
## 3 87398950 1.7 3.2 5.3 5.06 6.18 8.9
## 4 87398980 1.2 3.8 5.6 5.38 6.6 9.2
## 5 87405500 0.2 1.4 2.55 3.28 4 14.2
## 6 87406900 0 1.1 1.9 2.59 3.15 16
## 7 87409900 0 0.7 2.3 3.12 3.7 10.6## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.38 3.11 4.41 4.57 6.2 12.4
## 2 87398900 3.52 5.25 5.96 6.61 7.3 13.8
## 3 87398950 1.62 3.68 4.92 5.28 6.64 11.9
## 4 87398980 3.37 5.5 6.17 6.48 7.14 13.1
## 5 87405500 0.2 1.3 2.53 2.83 3.66 9.8
## 6 87406900 0.1 0.865 2.4 2.43 3.05 9.1
## 7 87409900 0.1 0.92 2.03 2.43 3.5 8.1Time for this code chunk to run: 0.249844074249268
8.0.2 Demanda Bioquímica de Oxigênio
(dbo <- ggplot(plan_wide_19902020,
aes(x = CODIGO,
y = DBO))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=10,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=5,
ymax=10,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3,
ymax=5,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2020",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(1,100),
trans = "log10")+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
## Removed 60 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 22 rows containing missing values (position_quasirandom).## Warning: Removed 30 rows containing missing values (position_quasirandom).## Warning: Removed 8 rows containing missing values (position_quasirandom).
Time for
this code chunk to run: 1.44011902809143
Time
for this code chunk to run: 0.710690975189209
Time
for this code chunk to run: 0.606827974319458
Time
for this code chunk to run: 0.673225879669189
Time
for this code chunk to run: 0.682582139968872
Time
for this code chunk to run: 0.666539907455444
Time
for this code chunk to run: 0.588973045349121
grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3)
Time
for this code chunk to run: 1.74743294715881
(sum_dbo_p1 <- plan_wide_19902020 %>%
select(CODIGO, DBO, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(DBO,
na.rm = TRUE),
q1 =
quantile(DBO, 0.25,
na.rm = TRUE),
median =
median(DBO,
na.rm = TRUE),
mean =
mean(DBO,
na.rm= TRUE),
q3 =
quantile(DBO, 0.75,
na.rm = TRUE),
max =
max(DBO,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 2 1.86 2 13
## 2 87398900 1 1 1 1.52 2 6
## 3 87398950 1 1 1 1.66 2 6
## 4 87398980 1 1 1 1.13 1 2
## 5 87405500 1 2 3 5.37 5 64
## 6 87406900 1 4 5 9 11 26
## 7 87409900 2 3 4 6.97 9.5 31(sum_dbo_p2 <- plan_wide_19902020 %>%
select(CODIGO, DBO, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(DBO,
na.rm = TRUE),
q1 =
quantile(DBO, 0.25,
na.rm = TRUE),
median =
median(DBO,
na.rm = TRUE),
mean =
mean(DBO,
na.rm= TRUE),
q3 =
quantile(DBO, 0.75,
na.rm = TRUE),
max =
max(DBO,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 1 1.58 2 5
## 2 87398900 1 1 1 1.40 2 5
## 3 87398950 1 1 1 1.66 2 5
## 4 87398980 1 1 1 1.30 1 5
## 5 87405500 1 2 4 4.67 6.5 14
## 6 87406900 1 3 5 6.53 8 28
## 7 87409900 1 3 6 6.31 9 15(sum_dbo_p3 <- plan_wide_19902020 %>%
select(CODIGO, DBO, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(DBO,
na.rm = TRUE),
q1 =
quantile(DBO, 0.25,
na.rm = TRUE),
median =
median(DBO,
na.rm = TRUE),
mean =
mean(DBO,
na.rm= TRUE),
q3 =
quantile(DBO, 0.75,
na.rm = TRUE),
max =
max(DBO,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 1 1 1.5 2.15 3 7
## 2 87398900 1 1 1 1.51 2 5
## 3 87398950 1 1 2 2.65 2 18
## 4 87398980 1 1 1 1.32 2 2
## 5 87405500 1 3 4 5.28 6.25 21
## 6 87406900 1 3 5 6.58 10 24
## 7 87409900 1 3 4.5 6.18 8 18Time for this code chunk to run: 0.183116912841797
ggsave("dbo.png",
units = c("px"),
width = 4500,
height = 2993,
plot = dbo,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("dbo_p1.png",
plot = dbo_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("dbo_p2.png",
plot = dbo_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("dbo_p3.png",
plot = dbo_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("dbo_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 6.46726298332214
8.0.3 Fósforo total
(ptot <- ggplot(plan_wide_19902020,
aes(CODIGO,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "Fósforo total no período 1990-2020",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 134 rows containing non-finite values (stat_boxplot).
## Removed 134 rows containing non-finite values (stat_boxplot).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 47 rows containing missing values (position_quasirandom).## Warning: Removed 31 rows containing missing values (position_quasirandom).## Warning: Removed 56 rows containing missing values (position_quasirandom).
Time for this code chunk to
run: 1.63830709457397
(ptot_p1<-ggplot(plan_wide_19902020%>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 1990-2000",
x="Estação",
y="mg/L")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10")+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.643969058990479
(ptot_p2 <- ggplot(plan_wide_19902020%>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 2000-2010",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.672133207321167
(ptot_p3 <- ggplot(plan_wide_19902020%>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
fosforo_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.15,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0.1,
ymax=0.15,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=0.1,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Fósforo total no período 2010-2020",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
trans = "log10")+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.593990087509155
grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3)
Time
for this code chunk to run: 1.65662002563477
(sum_ptot_p1 <- plan_wide_19902020 %>%
select(CODIGO, fosforo_total, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(fosforo_total, na.rm = TRUE),
q1 =
quantile(fosforo_total, 0.25, na.rm = TRUE),
median =
median(fosforo_total, na.rm = TRUE),
mean =
mean(fosforo_total, na.rm= TRUE),
q3 =
quantile(fosforo_total, 0.75, na.rm = TRUE),
max =
max(fosforo_total, na.rm = TRUE)))## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.0097 0.0593 0.0881 0.123 0.14 0.863
## 2 87398900 0.0023 0.0468 0.0678 0.0747 0.0883 0.247
## 3 87398950 0.0202 0.0544 0.0737 0.0751 0.0904 0.179
## 4 87398980 0.01 0.0254 0.0547 0.0708 0.114 0.189
## 5 87405500 0.017 0.171 0.281 0.417 0.492 2.32
## 6 87406900 0.156 0.270 0.508 0.785 1.07 2.79
## 7 87409900 0.107 0.258 0.384 0.489 0.712 1.53(sum_ptot_p2 <- plan_wide_19902020 %>%
select(CODIGO, fosforo_total, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(fosforo_total, na.rm = TRUE),
q1 =
quantile(fosforo_total, 0.25, na.rm = TRUE),
median =
median(fosforo_total, na.rm = TRUE),
mean =
mean(fosforo_total, na.rm= TRUE),
q3 =
quantile(fosforo_total, 0.75, na.rm = TRUE),
max =
max(fosforo_total, na.rm = TRUE)))## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.025 0.094 0.131 0.148 0.16 0.637
## 2 87398900 0.015 0.0764 0.104 0.140 0.164 0.646
## 3 87398950 0.036 0.116 0.171 0.180 0.207 0.485
## 4 87398980 0.0115 0.052 0.076 0.101 0.103 1
## 5 87405500 0.046 0.261 0.406 0.547 0.681 1.98
## 6 87406900 0.056 0.338 0.599 0.752 0.967 3.49
## 7 87409900 0.043 0.325 0.624 0.677 0.989 1.57(sum_ptot_p3 <- plan_wide_19902020 %>%
select(CODIGO, fosforo_total, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(fosforo_total, na.rm = TRUE),
q1 =
quantile(fosforo_total, 0.25, na.rm = TRUE),
median =
median(fosforo_total, na.rm = TRUE),
mean =
mean(fosforo_total, na.rm= TRUE),
q3 =
quantile(fosforo_total, 0.75, na.rm = TRUE),
max =
max(fosforo_total, na.rm = TRUE)))## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.061 0.118 0.163 0.166 0.186 0.381
## 2 87398900 0.057 0.0935 0.130 0.163 0.168 0.444
## 3 87398950 0.07 0.132 0.156 0.292 0.221 3.11
## 4 87398980 0.019 0.0625 0.106 0.144 0.170 0.59
## 5 87405500 0.013 0.187 0.332 0.361 0.45 0.803
## 6 87406900 0.089 0.254 0.364 0.448 0.560 1.26
## 7 87409900 0.203 0.259 0.369 0.488 0.564 1.7Time for this code chunk to run: 0.227257013320923
ggsave("ptot.png",
units = c("px"),
width = 4500,
height = 2993,
plot = ptot,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ptot_p1.png",
plot = ptot_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ptot_p2.png",
plot = ptot_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ptot_p3.png",
plot = ptot_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ptot_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 6.56522011756897
8.0.4 Escherichia coli
ecoli__class <- function() {
list(annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb") #classe 1
)
}
(ecoli <- ggplot(plan_wide_19902020,
aes(CODIGO,
E_coli))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "Escherichia coli no período 1990-2020",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
# n.breaks = 9,
n.breaks = 6,
limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
max(plan_wide_19902020$E_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()+
theme(
axis.text.y = element_text(
angle = 90,
# size=15,
# face=2
)
)
)
Time for this
code chunk to run: 1.49582481384277
(ecoli_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
E_coli))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 1990-2000",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
max(plan_wide_19902020$E_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.762883186340332
(ecoli_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
E_coli))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 2000-2010",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
max(plan_wide_19902020$E_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.674049139022827
(ecoli_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
E_coli))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3200,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=800,
ymax=3200,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=160,
ymax=800,
alpha=1,
fill="#70c18c")+ #classe 2
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=160,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Escherichia coli no período 2010-2020",
x="Estação",
y="NMP/100mL")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
max(plan_wide_19902020$E_coli, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.644150972366333
grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3)
Time
for this code chunk to run: 1.79713010787964
(sum_ecoli_p1 <- plan_wide_19902020 %>%
select(CODIGO, E_coli, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(E_coli,
na.rm = TRUE),
q1 =
quantile(E_coli, 0.25,
na.rm = TRUE),
median =
median(E_coli,
na.rm = TRUE),
mean =
mean(E_coli,
na.rm= TRUE),
q3 =
quantile(E_coli, 0.75,
na.rm = TRUE),
max =
max(E_coli,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 32 136 240 854. 720 19200
## 2 87398900 16 68 160 548. 480 7760
## 3 87398950 2.4 12.8 268 4039. 10000 28000
## 4 87398980 4 160 243. 2907. 446 25600
## 5 87405500 1.6 12.8 24 545. 128 18400
## 6 87406900 13.6 61.6 192 718. 414 12800
## 7 87409900 2.4 12.8 64 97.7 128 720(sum_ecoli_p2 <- plan_wide_19902020 %>%
select(CODIGO, E_coli, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(E_coli,
na.rm = TRUE),
q1 =
quantile(E_coli, 0.25,
na.rm = TRUE),
median =
median(E_coli,
na.rm = TRUE),
mean =
mean(E_coli,
na.rm= TRUE),
q3 =
quantile(E_coli, 0.75,
na.rm = TRUE),
max =
max(E_coli,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 21.6 91 150 1335. 308 27200
## 2 87398900 11 70 133. 444. 414. 2600
## 3 87398950 20 400 720 935. 1120 5500
## 4 87398980 24 110. 195 410. 289. 8800
## 5 87405500 4.7 162 2400 25445. 12950 490000
## 6 87406900 8 172 12800 66370. 62300 650000
## 7 87409900 16 7355. 35500 72440. 68750 460000(sum_ecoli_p3 <- plan_wide_19902020 %>%
select(CODIGO, E_coli, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(E_coli,
na.rm = TRUE),
q1 =
quantile(E_coli, 0.25,
na.rm = TRUE),
median =
median(E_coli,
na.rm = TRUE),
mean =
mean(E_coli,
na.rm= TRUE),
q3 =
quantile(E_coli, 0.75,
na.rm = TRUE),
max =
max(E_coli,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 90 155. 260 409. 451 2420
## 2 87398900 10 52.8 107 245. 313 1553.
## 3 87398950 108. 250 487 1424. 1553. 10462
## 4 87398980 40.8 140. 242. 529. 738. 2400
## 5 87405500 632 8965 19232. 109992. 70750 1400000
## 6 87406900 1440 23100 34500 230828. 140500 3400000
## 7 87409900 2000 20100 38400 83128. 83680 345000Time for this code chunk to run: 0.239304065704346
ggsave("ecoli.png",
plot = ecoli,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ecoli_p1.png",
plot = ecoli_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ecoli_p2.png",
plot = ecoli_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ecoli_p3.png",
plot = ecoli_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("ecoli_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 6.30872392654419
8.0.5 Nitrogênio amoniacal
(namon <- ggplot(plan_wide_19902020,
aes(CODIGO,
nitro_amon))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=13.3,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3.7,
ymax=13.3,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3.7,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "Nitrogênio amoniacal no período 1990-2020",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time for this code
chunk to run: 1.30818915367126
periodo_inicial <- as.Date("1990-01-01", "%Y-%m-%d")
periodo_final <- as.Date("2021-01-01", "%Y-%m-%d")
(nitro_line <-
plan_wide_19902020 %>%
filter(ANO_COLETA > "1990" &
ANO_COLETA <= "2020") %>%
select(CODIGO, nitro_amon, DATA_COLETA, periodo) %>%
group_by(CODIGO) %>%
# pivot_wider(
# names_from = CODIGO,
# values_from = nitro_amon,
# id_cols = DATA_COLETA
# ) %>%
ggplot(
aes(x = DATA_COLETA,
y = nitro_amon,
# color = CODIGO
))+
# geom_rect(
# aes(xmin = periodo_inicial,
# xmax = periodo_final,
# ymin = 13.3,
# ymax = Inf,
# alpha= 0.005,
# fill= "#ac5079"),
# show.legend = FALSE)+ #>pior classe
# geom_rect(
# aes(xmin = periodo_inicial,
# xmax = periodo_final,
# ymin= 3.7,
# ymax= 13.3,
# alpha= 0.005,
# fill= "#fcf7ab"),
# show.legend = FALSE)+ #classe 3
# geom_rect(
# aes(xmin = periodo_inicial,
# xmax = periodo_final,
# ymin= 0,
# ymax= 3.7,
# alpha= 0.005,
# fill= "blue"
# # "#8dcdeb"
# ),
# show.legend = FALSE)+ #classe 1
annotate("rect",
xmin= periodo_inicial,
xmax= periodo_final,
ymin=13.3,
ymax=Inf,
alpha= 0.7,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin= periodo_inicial,
xmax= periodo_final,
ymin=3.7,
ymax=13.3,
alpha= 0.7,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin= periodo_inicial,
xmax= periodo_final,
ymin= -Inf,
ymax=3.7,
alpha= 0.7,
fill="#8dcdeb")+ #classe 1
geom_line(
# aes(color = CODIGO),
na.rm = TRUE)+
geom_point(
# aes(color = CODIGO),
na.rm = TRUE)+
scale_x_date(
limits = as.Date(c(
"1990-01-01",
"2021-01-01"
# NA #pode usar NA também
)),
expand = c(0.0, 0.0),
date_breaks = "10 years",
minor_breaks = "5 years",
date_labels = "%Y",
)+
# geom_smooth(
# # aes(color = CODIGO),
# method = "lm",
# # formula = y ~ poly(x, 2),
# # span = 0.2,
# se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
# aes(group = 1),
# alpha =.5,
# na.rm = TRUE,
# size = 0.3,
# # fullrange = TRUE,
# # show.legend = TRUE
# )+
# stat_smooth(
# geom = "smooth",
# # span = 0.2,
# se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
# # aes(group = 1),
# # alpha =.5,
# na.rm = TRUE,
# # size = 0.3,
# fullrange = TRUE,
# show.legend = TRUE
# )+
facet_wrap(
~CODIGO,
nrow = 4,
)+
theme_bw()
)
Time
for this code chunk to run: 1.26200008392334
(namon_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
nitro_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=13.3,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3.7,
ymax=13.3,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3.7,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Nitrogênio amoniacal no período 1990-2000",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.698621034622192
(namon_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
nitro_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=13.3,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3.7,
ymax=13.3,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3.7,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Nitrogênio amoniacal no período 2000-2010",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.818024158477783
(namon_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
nitro_total))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=13.3,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=3.7,
ymax=13.3,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=3.7,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Nitrogênio amoniacal no período 2010-2020",
x="Estação",
y="mg/L")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 9,
limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = .001,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.525738000869751
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
Time
for this code chunk to run: 1.52824997901917
(sum_namon_p1 <- plan_wide_19902020 %>%
select(CODIGO, nitro_total, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(nitro_total,
na.rm = TRUE),
q1 =
quantile(nitro_total, 0.25,
na.rm = TRUE),
median =
median(nitro_total,
na.rm = TRUE),
mean =
mean(nitro_total,
na.rm= TRUE),
q3 =
quantile(nitro_total, 0.75,
na.rm = TRUE),
max =
max(nitro_total,
na.rm = TRUE),
n =
length(nitro_total)
)
)## # A tibble: 7 x 8
## CODIGO min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 0.44 0.842 1.00 1.22 1.34 3.81 101
## 2 87398900 0.22 0.82 1 1.09 1.25 4.86 101
## 3 87398950 0.51 0.83 1.02 1.06 1.19 2.16 68
## 4 87398980 0.549 0.68 0.755 0.872 1.01 1.85 30
## 5 87405500 0.51 1.53 2.94 5.27 6.77 21.6 97
## 6 87406900 1.34 2.60 4.56 7.58 11.2 29.1 32
## 7 87409900 0.5 1.98 4.29 5.18 7.01 19.6 65(sum_namon_p2 <- plan_wide_19902020 %>%
select(CODIGO, nitro_total, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(nitro_total,
na.rm = TRUE),
q1 =
quantile(nitro_total, 0.25,
na.rm = TRUE),
median =
median(nitro_total,
na.rm = TRUE),
mean =
mean(nitro_total,
na.rm= TRUE),
q3 =
quantile(nitro_total, 0.75,
na.rm = TRUE),
max =
max(nitro_total,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.18 0.885 0.992 1.80 1.46 23.2
## 2 87398900 0.48 0.894 1.13 1.38 1.57 7.92
## 3 87398950 0.57 1.26 1.45 1.43 1.71 1.98
## 4 87398980 0.19 0.685 0.79 1.05 1.10 5.2
## 5 87405500 0.968 2 3.29 5.45 6.60 21.7
## 6 87406900 0.77 2.4 4.54 7.30 10.2 39.1
## 7 87409900 1.62 2.5 6.97 7.92 10.6 21.5(sum_namon_p3 <- plan_wide_19902020 %>%
select(CODIGO, nitro_total, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(nitro_total,
na.rm = TRUE),
q1 =
quantile(nitro_total, 0.25,
na.rm = TRUE),
median =
median(nitro_total,
na.rm = TRUE),
mean =
mean(nitro_total,
na.rm= TRUE),
q3 =
quantile(nitro_total, 0.75,
na.rm = TRUE),
max =
max(nitro_total,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.222 0.89 1.11 1.24 1.41 2.56
## 2 87398900 0.095 0.883 1.02 1.29 1.40 4.25
## 3 87398950 0.612 1.04 1.43 1.90 2.06 9.5
## 4 87398980 0.216 0.973 1.12 1.22 1.58 2.32
## 5 87405500 1.12 2.03 3.14 4.50 5.93 22.0
## 6 87406900 1.37 2.40 5.58 6.47 7.58 25
## 7 87409900 1.11 3 6.15 7.29 7.75 36Time for this code chunk to run: 0.161532878875732
ggsave("namon.png",
units = c("px"),
width = 4500,
height = 2993,
plot = namon,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_p1.png",
plot = namon_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_p2.png",
plot = namon_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_p3.png",
plot = namon_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("namon_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 6.03347897529602
8.0.6 Turbidez
(turb <- ggplot(plan_wide_19902020,
aes(CODIGO,
turbidez))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=100,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=40,
ymax=100,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=40,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "Turbidez no período 1990-2020",
x="Estação",
y="UNT")+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.05)),
n.breaks = 8,
limits = c(
# 1,
min(plan_wide_19902020$turbidez, na.rm = TRUE),
# 500
max(plan_wide_19902020$turbidez, na.rm = TRUE)
),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time for this code chunk
to run: 1.33978581428528
(turb_line <- plan_wide_19902020 %>%
filter(ANO_COLETA > "1990" &
ANO_COLETA <= "2020") %>%
select(CODIGO, turbidez, DATA_COLETA, periodo) %>%
group_by(CODIGO) %>%
ggplot(
aes(x = DATA_COLETA,
y = turbidez,
color = CODIGO
))+
geom_line(
# aes(color = CODIGO),
na.rm = TRUE)+
geom_point(
# aes(color = CODIGO),
na.rm = TRUE)+
scale_x_date(
limits = as.Date(c(
"1990-01-01",
"2021-01-01"
# NA #pode usar NA também
)),
expand = c(0.0, 0.0),
date_breaks = "10 years",
minor_breaks = "5 years",
date_labels = "%Y",
)+
# geom_smooth(
# # aes(color = CODIGO),
# method = "lm",
# # formula = y ~ poly(x, 2),
# # span = 0.2,
# se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
# aes(group = 1),
# alpha =.5,
# na.rm = TRUE,
# size = 0.3,
# # fullrange = TRUE,
# # show.legend = TRUE
# )+
# stat_smooth(
# geom = "smooth",
# # span = 0.2,
# se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
# # aes(group = 1),
# # alpha =.5,
# na.rm = TRUE,
# # size = 0.3,
# fullrange = TRUE,
# show.legend = TRUE
# )+
facet_wrap(
~CODIGO,
nrow = 4,
)+
theme_bw()
)
Time
for this code chunk to run: 1.41581416130066
(turb_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
turbidez))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=100,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=40,
ymax=100,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=40,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Turbidez no período 1990-2000",
x="Estação",
y="UNT")+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
max(plan_wide_19902020$turbidez, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.681066989898682
(turb_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
turbidez))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=100,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=40,
ymax=100,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=40,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Turbidez no período 2000-2010",
x="Estação",
y="UNT")+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
max(plan_wide_19902020$turbidez, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.598002910614014
(turb_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
turbidez))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=100,
ymax=Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=40,
ymax=100,
alpha=1,
fill="#fcf7ab")+ #classe 3
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=0,
ymax=40,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Turbidez no período 2010-2020",
x="Estação",
y="UNT")+
scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
n.breaks = 8,
limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
max(plan_wide_19902020$turbidez, na.rm = TRUE)),
trans = "log10",
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.60231614112854
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
Time
for this code chunk to run: 1.71806907653809
(sum_turb_p1 <- plan_wide_19902020 %>%
select(CODIGO, turbidez, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(turbidez,
na.rm = TRUE),
q1 =
quantile(turbidez, 0.25,
na.rm = TRUE),
median =
median(turbidez,
na.rm = TRUE),
mean =
mean(turbidez,
na.rm= TRUE),
q3 =
quantile(turbidez, 0.75,
na.rm = TRUE),
max =
max(turbidez,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 6.2 19 34.5 63.5 67 461
## 2 87398900 9 19 49.5 61.5 73.8 460
## 3 87398950 9.6 16 22 33.3 48.8 144
## 4 87398980 16 32.8 43 66.8 90.5 190
## 5 87405500 8.5 23.5 47 47.5 58 159
## 6 87406900 33 54.8 67 77.7 81.5 199
## 7 87409900 5.8 15 25 32.2 48 76(sum_turb_p2 <- plan_wide_19902020 %>%
select(CODIGO, turbidez, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(turbidez,
na.rm = TRUE),
q1 =
quantile(turbidez, 0.25,
na.rm = TRUE),
median =
median(turbidez,
na.rm = TRUE),
mean =
mean(turbidez,
na.rm= TRUE),
q3 =
quantile(turbidez, 0.75,
na.rm = TRUE),
max =
max(turbidez,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 9 41.2 55.5 71.1 74.2 428
## 2 87398900 39 57 78 107. 116. 475
## 3 87398950 39 47 64 96.5 90 330
## 4 87398980 24 37 50 64.5 87 176
## 5 87405500 32 46 63.5 70.3 76 341
## 6 87406900 35 49 62 69.9 75.5 284
## 7 87409900 40 45 60 70.4 90 151(sum_turb_p3 <- plan_wide_19902020 %>%
select(CODIGO, turbidez, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(turbidez,
na.rm = TRUE),
q1 =
quantile(turbidez, 0.25,
na.rm = TRUE),
median =
median(turbidez,
na.rm = TRUE),
mean =
mean(turbidez,
na.rm= TRUE),
q3 =
quantile(turbidez, 0.75,
na.rm = TRUE),
max =
max(turbidez,
na.rm = TRUE))
) ## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 8.52 16.4 29 33.3 43 85
## 2 87398900 14.8 39.2 48.3 66.7 73.4 299
## 3 87398950 16 29.9 41 51.6 65 230
## 4 87398980 11 19.4 33.6 39.5 42.2 110.
## 5 87405500 10.0 29.0 41 42.9 54.5 131
## 6 87406900 9.62 23 39 41.2 52 122
## 7 87409900 9.68 22.0 34.0 40.5 47 182.Time for this code chunk to run: 0.205184936523438
ggsave("turb.png",
units = c("px"),
width = 4500,
height = 2993,
plot = turb,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_p1.png",
plot = turb_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_p2.png",
plot = turb_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_p3.png",
plot = turb_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("turb_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 6.37820100784302
8.0.7 pH
(pH <- ggplot(plan_wide_19902020,
aes(CODIGO,
pH))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=6,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=9,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=9,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "pH no período 1990-2020",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 8,
limits = c(4,11),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time for this code chunk to
run: 1.48158502578735
(pH_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
pH))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=6,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=9,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=9,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "pH no período 1990-2000",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 8,
limits = c(4,11),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.708876132965088
(pH_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
pH))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=6,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=9,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=9,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "pH no período 2000-2010",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 8,
limits = c(4,11),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.64001202583313
(pH_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
pH))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=6,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=9,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=6,
ymax=9,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "pH no período 2010-2020",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
n.breaks = 8,
limits = c(4,11),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.608316898345947
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
Time
for this code chunk to run: 1.68490719795227
(sum_pH_p1 <- plan_wide_19902020 %>%
select(CODIGO, pH, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5 6.18 6.59 6.51 6.82 7.9
## 2 87398900 5.2 6 6.3 6.33 6.63 7.9
## 3 87398950 5.4 6.29 6.4 6.49 6.72 8.1
## 4 87398980 5.3 5.93 6.2 6.16 6.3 7.3
## 5 87405500 5 6.3 6.4 6.47 6.7 9.3
## 6 87406900 5.5 6.18 6.45 6.43 6.8 7.3
## 7 87409900 4.5 6.2 6.4 6.44 6.7 7.4(sum_pH_p2 <- plan_wide_19902020 %>%
select(CODIGO, pH, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
) ## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5.3 6.3 6.6 6.59 6.88 7.9
## 2 87398900 5.5 6.4 6.65 6.63 6.9 7.5
## 3 87398950 6 6.6 6.8 6.89 7.25 7.6
## 4 87398980 5.8 6.3 6.5 6.63 7 7.5
## 5 87405500 5.2 6.4 6.6 6.68 6.9 8.3
## 6 87406900 5.5 6.4 6.7 6.66 6.9 8.6
## 7 87409900 5.8 6.5 6.8 6.77 7 8.4(sum_pH_p3 <- plan_wide_19902020 %>%
select(CODIGO, pH, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(pH,
na.rm = TRUE),
q1 =
quantile(pH, 0.25,
na.rm = TRUE),
median =
median(pH,
na.rm = TRUE),
mean =
mean(pH,
na.rm= TRUE),
q3 =
quantile(pH, 0.75,
na.rm = TRUE),
max =
max(pH,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 5.47 6.28 6.42 6.47 6.60 7.3
## 2 87398900 5.68 6.36 6.5 6.57 6.84 7.4
## 3 87398950 5.71 6.28 6.46 6.46 6.68 7
## 4 87398980 5.42 6.10 6.36 6.39 6.6 7.2
## 5 87405500 5.64 6.34 6.5 6.49 6.7 7.01
## 6 87406900 5.6 6.4 6.48 6.51 6.77 7.3
## 7 87409900 5.59 6.46 6.6 6.57 6.76 7.2Time for this code chunk to run: 0.218722105026245
ggsave("pH.png",
units = c("px"),
width = 4500,
height = 2993,
plot = pH,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_p1.png",
plot = pH_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_p2.png",
plot = pH_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_p3.png",
plot = pH_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("pH_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time for
this code chunk to run: 6.68246579170227
8.0.8 Sólidos totais
(SolTot <- ggplot(plan_wide_19902020,
aes(CODIGO,
solidos_totais))+
annotate("rect",
xmin = -Inf, xmax = Inf,
ymin = 500, ymax = Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "Sólidos totais no período 1990-2020",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time for this code
chunk to run: 1.50272822380066
(SolTot_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000"),
aes(CODIGO,
solidos_totais))+
annotate("rect",
xmin = -Inf, xmax = Inf,
ymin = 500, ymax = Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Sólidos totais no período 1990-2000",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.691815853118896
(SolTot_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
solidos_totais))+
annotate("rect",
xmin = -Inf, xmax = Inf,
ymin = 500, ymax = Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Sólidos totais no período 2000-2010",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.648500919342041
(SolTot_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
solidos_totais))+
annotate("rect",
xmin = -Inf, xmax = Inf,
ymin = 500, ymax = Inf,
alpha=1,
fill="#ac5079")+ #>pior classe
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Sólidos totais no período 2010-2020",
x="Estação",
y="")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.582292079925537
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
Time
for this code chunk to run: 1.76624298095703
(sum_SolTot_p1 <- plan_wide_19902020 %>%
select(CODIGO, solidos_totais, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(solidos_totais,
na.rm = TRUE),
q1 =
quantile(solidos_totais, 0.25,
na.rm = TRUE),
median =
median(solidos_totais,
na.rm = TRUE),
mean =
mean(solidos_totais,
na.rm= TRUE),
q3 =
quantile(solidos_totais, 0.75,
na.rm = TRUE),
max =
max(solidos_totais,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 46 84.5 95 122. 120 510
## 2 87398900 18 74.5 97 111. 122. 474
## 3 87398950 10 76.5 91 90.9 106. 155
## 4 87398980 48 63.5 81.5 104. 126. 337
## 5 87405500 70 101 121 133. 151 361
## 6 87406900 89 118 155 165. 210 279
## 7 87409900 20 99.5 122 128. 143 381(sum_SolTot_p2 <- plan_wide_19902020 %>%
select(CODIGO, solidos_totais, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(solidos_totais,
na.rm = TRUE),
q1 =
quantile(solidos_totais, 0.25,
na.rm = TRUE),
median =
median(solidos_totais,
na.rm = TRUE),
mean =
mean(solidos_totais,
na.rm= TRUE),
q3 =
quantile(solidos_totais, 0.75,
na.rm = TRUE),
max =
max(solidos_totais,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 28 80 100 111. 123. 412
## 2 87398900 42 82 102. 128. 140. 489
## 3 87398950 46 94.2 108. 126. 127. 318
## 4 87398980 40 61 77 85.3 96 228
## 5 87405500 48 102 133 148. 170. 522
## 6 87406900 50 109 134. 154. 170. 670
## 7 87409900 56 112. 156 167. 190. 599(sum_SolTot_p3 <- plan_wide_19902020 %>%
select(CODIGO, solidos_totais, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(solidos_totais,
na.rm = TRUE),
q1 =
quantile(solidos_totais, 0.25,
na.rm = TRUE),
median =
median(solidos_totais,
na.rm = TRUE),
mean =
mean(solidos_totais,
na.rm= TRUE),
q3 =
quantile(solidos_totais, 0.75,
na.rm = TRUE),
max =
max(solidos_totais,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 61 69 90 82.8 96 101
## 2 87398900 41 77 104 120. 127 308
## 3 87398950 45 93 101 109. 117 221
## 4 87398980 55 62.8 80 79.9 95 109
## 5 87405500 83 89.2 108. 124. 162. 195
## 6 87406900 50 106 117 135. 163 246
## 7 87409900 75 103 115 131. 145 251Time for this code chunk to run: 0.341530799865723
ggsave("SolTot.png",
units = c("px"),
width = 4500,
height = 2993,
plot = SolTot,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_p1.png",
plot = SolTot_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_p2.png",
plot = SolTot_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_p3.png",
plot = SolTot_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("SolTot_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 6.73349714279175
8.0.9 IQA
Time for this code chunk to
run: 1.28392815589905
Time
for this code chunk to run: 0.666742086410522
Time
for this code chunk to run: 0.538616895675659
Time
for this code chunk to run: 0.552359104156494
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
Time
for this code chunk to run: 1.6210241317749
(sum_IQA_p1 <- plan_wide_19902020 %>%
select(CODIGO, IQA, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(IQA,
na.rm = TRUE),
q1 =
quantile(IQA, 0.25,
na.rm = TRUE),
median =
median(IQA,
na.rm = TRUE),
mean =
mean(IQA,
na.rm= TRUE),
q3 =
quantile(IQA, 0.75,
na.rm = TRUE),
max =
max(IQA,
na.rm = TRUE),
n =
length(IQA)
)
)## # A tibble: 7 x 8
## CODIGO min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 27.0 35.7 40.9 40.7 46.2 52.2 101
## 2 87398900 27.8 37.9 42.9 43.0 48.0 58.5 101
## 3 87398950 32.8 36.8 41.4 43.2 48.6 61.9 68
## 4 87398980 29.2 35.8 40.4 40.3 44.8 51.9 30
## 5 87405500 24.8 34.9 41.2 40.3 46.9 57.6 97
## 6 87406900 24.7 31.3 37.8 37.4 44.4 49.0 32
## 7 87409900 23.6 31.9 37.1 38.8 46.2 55.4 65(sum_IQA_p2 <- plan_wide_19902020 %>%
select(CODIGO, IQA, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(IQA,
na.rm = TRUE),
q1 =
quantile(IQA, 0.25,
na.rm = TRUE),
median =
median(IQA,
na.rm = TRUE),
mean =
mean(IQA,
na.rm= TRUE),
q3 =
quantile(IQA, 0.75,
na.rm = TRUE),
max =
max(IQA,
na.rm = TRUE),
n =
length(IQA)
)
)## # A tibble: 7 x 8
## CODIGO min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 27.8 34.6 40.0 39.5 43.5 48.7 75
## 2 87398900 28.5 35.1 37.6 38.3 40.6 48.5 77
## 3 87398950 21.1 29.4 32.7 32.8 36.8 44.0 30
## 4 87398980 24.5 35.7 39.4 39.5 43.4 52.1 66
## 5 87405500 19.8 28.7 31.5 31.9 35.7 48.8 78
## 6 87406900 17.1 25.3 29.0 29.5 32.8 44.1 79
## 7 87409900 16.2 20.5 26.1 25.0 29.8 33.1 31(sum_IQA_p3 <- plan_wide_19902020 %>%
select(CODIGO, IQA, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
# ?as_factor(CODIGO) %>%
group_by(CODIGO) %>%
summarize(
min =
min(IQA,
na.rm = TRUE),
q1 =
quantile(IQA, 0.25,
na.rm = TRUE),
median =
median(IQA,
na.rm = TRUE),
mean =
mean(IQA,
na.rm= TRUE),
q3 =
quantile(IQA, 0.75,
na.rm = TRUE),
max =
max(IQA,
na.rm = TRUE),
n =
length(IQA),
NAs =
sum(is.na(IQA))
) %>%
mutate(
"%NA" = NAs/n*100
)
)## # A tibble: 7 x 10
## CODIGO min q1 median mean q3 max n NAs `%NA`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
## 1 87398500 40.2 42.5 45.4 44.2 45.5 47.2 34 29 85.3
## 2 87398900 34.1 38.6 41.2 40.2 42.9 44.4 36 32 88.9
## 3 87398950 36.7 39.5 42.4 41.5 44.4 44.6 35 31 88.6
## 4 87398980 40.0 40.0 40.0 40.0 40.0 40.0 28 27 96.4
## 5 87405500 30.8 31.6 32.5 32.5 33.3 34.1 33 31 93.9
## 6 87406900 22.9 24.4 25.9 25.3 26.5 27.2 35 32 91.4
## 7 87409900 24.1 25.1 27.3 26.9 28.2 29.7 37 32 86.5Time for this code chunk to run: 0.215217113494873
ggsave("iqa.png",
units = c("px"),
width = 4500,
height = 2993,
plot = iqa,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_p1.png",
plot = iqa_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_p2.png",
plot = iqa_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_p3.png",
plot = iqa_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("iqa_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 5.96022009849548
8.1 Testando coisas
8.1.1 Correlação
parametros_IQA %>%
select(
-CODIGO,
-nitro_total) %>%
# group_by(CODIGO) %>%
rename(
CE = Condutividade,
OD = oxigenio_dissolvido,
ST = solidos_totais,
Turb = turbidez,
Temp = temp_agua,
Ptot = fosforo_total,
NAmon = nitro_amon,
NTK = nitro_kjeldahl
) %>%
ggcorr(
method = "complete.obs",
# "pearson",
# "pairwise",
name = "Correlação",
label = TRUE,
label_alpha = TRUE,
digits = 3,
low = "#3B9AB2",
mid = "#EEEEEE",
high = "#F21A00",
# palette = "RdYlBu",
layout.exp = 0,
legend.position = "left",
label_round = 3,
# legend.size = 18,
geom = "tile",
nbreaks = 10,
)+
labs(title = "Correlação entre parâmetros físico-químicos na\nBacia Hidrográfica do rio Gravataí no período 1990-2020")+
theme_linedraw()+
theme(
legend.position = c(0.15, 0.6),
legend.title = element_text(size = 16),
legend.text = element_text(size = 14),
# legend.spacing = unit(element_text(),
# units = 5)
plot.title = element_text(hjust = 0.5,
size = 16)
)
correlação-parametros-qualidade-agua-gravataí no período 1990-2020
# Gráfico das correlações entre todos os parâmetros com significância
correl_IQA <- parametros_IQA %>%
select(-CODIGO) %>%
ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
axisLabels = "show")
correlacao_pIQA <- parametros_IQA %>%
group_by(CODIGO) %>%
correlation::correlation()
correlacao_pIQA %>%
# glimpse()
filter(
p < 0.001
) %>%
t() %>%
summary()## 87398500 - DBO 87398500 - nitro_kjeldahl 87398500 - nitro_total
## Min. :0.4176 Min. :0.4105 Min. :0.4915
## 1st Qu.:1.0000 1st Qu.:0.7854 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.9552 Mean :0.8634 Mean :0.9609
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## 87398500 - fosforo_total 87398500 - temp_agua 87398500 - turbidez
## Min. :0.3641 Min. :-0.4765 Min. :0.3641
## 1st Qu.:1.0000 1st Qu.: 1.0000 1st Qu.:1.0000
## Median :1.0000 Median : 1.0000 Median :1.0000
## Mean :0.8668 Mean : 0.8864 Mean :0.9133
## 3rd Qu.:1.0000 3rd Qu.: 1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. : 1.0000 Max. :1.0000
## 87398500 - oxigenio_dissolvido 87398500 - nitro_amon 87398500 - pH
## Min. :-0.4765 Min. :1 Min. :1
## 1st Qu.: 1.0000 1st Qu.:1 1st Qu.:1
## Median : 1.0000 Median :1 Median :1
## Mean : 0.7836 Mean :1 Mean :1
## 3rd Qu.: 1.0000 3rd Qu.:1 3rd Qu.:1
## Max. : 1.0000 Max. :1 Max. :1
## 87398500 - solidos_totais 87398500 - E_coli 87398500 - ANO_COLETA
## Min. :0.4915 Min. :1 Min. :-0.3361
## 1st Qu.:0.8978 1st Qu.:1 1st Qu.: 1.0000
## Median :1.0000 Median :1 Median : 1.0000
## Mean :0.8976 Mean :1 Mean : 0.8972
## 3rd Qu.:1.0000 3rd Qu.:1 3rd Qu.: 1.0000
## Max. :1.0000 Max. :1 Max. : 1.0000
## 87398500 - Condutividade 87398900 - DBO 87398900 - nitro_kjeldahl
## Min. :1 Min. :0.3880 Min. :0.4385
## 1st Qu.:1 1st Qu.:1.0000 1st Qu.:0.9982
## Median :1 Median :1.0000 Median :1.0000
## Mean :1 Mean :0.9529 Mean :0.9000
## 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1 Max. :1.0000 Max. :1.0000
## 87398900 - nitro_total 87398900 - fosforo_total 87398900 - temp_agua
## Min. :0.4067 Min. :0.3526 Min. :-0.3893
## 1st Qu.:0.9982 1st Qu.:0.5279 1st Qu.: 1.0000
## Median :1.0000 Median :1.0000 Median : 1.0000
## Mean :0.8647 Mean :0.8295 Mean : 0.8931
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 1.0000
## Max. :1.0000 Max. :1.0000 Max. : 1.0000
## 87398900 - turbidez 87398900 - oxigenio_dissolvido 87398900 - nitro_amon
## Min. :0.4067 Min. :-0.3893 Min. :0.3880
## 1st Qu.:0.9070 1st Qu.: 1.0000 1st Qu.:0.7489
## Median :1.0000 Median : 1.0000 Median :1.0000
## Mean :0.8634 Mean : 0.8931 Mean :0.8447
## 3rd Qu.:1.0000 3rd Qu.: 1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. : 1.0000 Max. :1.0000
## 87398900 - pH 87398900 - solidos_totais 87398900 - E_coli
## Min. :1 Min. :0.4234 Min. :1
## 1st Qu.:1 1st Qu.:0.9070 1st Qu.:1
## Median :1 Median :1.0000 Median :1
## Mean :1 Mean :0.8748 Mean :1
## 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1
## Max. :1 Max. :1.0000 Max. :1
## 87398900 - ANO_COLETA 87398900 - Condutividade 87398950 - DBO
## Min. :0.3526 Min. :1 Min. :1
## 1st Qu.:1.0000 1st Qu.:1 1st Qu.:1
## Median :1.0000 Median :1 Median :1
## Mean :0.9502 Mean :1 Mean :1
## 3rd Qu.:1.0000 3rd Qu.:1 3rd Qu.:1
## Max. :1.0000 Max. :1 Max. :1
## 87398950 - nitro_kjeldahl 87398950 - nitro_total 87398950 - fosforo_total
## Min. :-0.5359 Min. :0.5497 Min. :0.5497
## 1st Qu.: 1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median : 1.0000 Median :1.0000 Median :1.0000
## Mean : 0.8593 Mean :0.9647 Mean :0.9307
## 3rd Qu.: 1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. : 1.0000 Max. :1.0000 Max. :1.0000
## 87398950 - temp_agua 87398950 - turbidez 87398950 - oxigenio_dissolvido
## Min. :-0.5945 Min. :0.8455 Min. :-0.5945
## 1st Qu.: 1.0000 1st Qu.:1.0000 1st Qu.: 1.0000
## Median : 1.0000 Median :1.0000 Median : 1.0000
## Mean : 0.8773 Mean :0.9881 Mean : 0.6475
## 3rd Qu.: 1.0000 3rd Qu.:1.0000 3rd Qu.: 1.0000
## Max. : 1.0000 Max. :1.0000 Max. : 1.0000
## 87398950 - nitro_amon 87398950 - pH 87398950 - solidos_totais
## Min. :1 Min. :1 Min. :0.8455
## 1st Qu.:1 1st Qu.:1 1st Qu.:1.0000
## Median :1 Median :1 Median :1.0000
## Mean :1 Mean :1 Mean :0.9881
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1.0000
## Max. :1 Max. :1 Max. :1.0000
## 87398950 - E_coli 87398950 - ANO_COLETA 87398950 - Condutividade
## Min. :1 Min. :1 Min. :-0.4515
## 1st Qu.:1 1st Qu.:1 1st Qu.: 1.0000
## Median :1 Median :1 Median : 1.0000
## Mean :1 Mean :1 Mean : 0.8318
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.: 1.0000
## Max. :1 Max. :1 Max. : 1.0000
## 87398980 - DBO 87398980 - nitro_kjeldahl 87398980 - nitro_total
## Min. :1 Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1 1st Qu.:1
## Median :1 Median :1 Median :1
## Mean :1 Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1 Max. :1
## 87398980 - fosforo_total 87398980 - temp_agua 87398980 - turbidez
## Min. :1 Min. :0.5681 Min. :0.5681
## 1st Qu.:1 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1 Median :1.0000 Median :1.0000
## Mean :1 Mean :0.9668 Mean :0.9402
## 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1 Max. :1.0000 Max. :1.0000
## 87398980 - oxigenio_dissolvido 87398980 - nitro_amon 87398980 - pH
## Min. :1 Min. :1 Min. :1
## 1st Qu.:1 1st Qu.:1 1st Qu.:1
## Median :1 Median :1 Median :1
## Mean :1 Mean :1 Mean :1
## 3rd Qu.:1 3rd Qu.:1 3rd Qu.:1
## Max. :1 Max. :1 Max. :1
## 87398980 - solidos_totais 87398980 - E_coli 87398980 - ANO_COLETA
## Min. :0.6547 Min. :1 Min. :1
## 1st Qu.:1.0000 1st Qu.:1 1st Qu.:1
## Median :1.0000 Median :1 Median :1
## Mean :0.9734 Mean :1 Mean :1
## 3rd Qu.:1.0000 3rd Qu.:1 3rd Qu.:1
## Max. :1.0000 Max. :1 Max. :1
## 87398980 - Condutividade 87405500 - DBO 87405500 - nitro_kjeldahl
## Min. :1 Min. :0.4199 Min. :0.3930
## 1st Qu.:1 1st Qu.:0.5402 1st Qu.:0.6656
## Median :1 Median :1.0000 Median :0.9319
## Mean :1 Mean :0.7900 Mean :0.8206
## 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1 Max. :1.0000 Max. :1.0000
## 87405500 - nitro_total 87405500 - fosforo_total 87405500 - temp_agua
## Min. :0.5052 Min. :-0.3314 Min. :0.3109
## 1st Qu.:0.6036 1st Qu.: 0.4828 1st Qu.:0.4534
## Median :0.9033 Median : 0.7834 Median :0.5711
## Mean :0.8197 Mean : 0.6601 Mean :0.7138
## 3rd Qu.:1.0000 3rd Qu.: 1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. : 1.0000 Max. :1.0000
## 87405500 - turbidez 87405500 - oxigenio_dissolvido 87405500 - nitro_amon
## Min. :0.3797 Min. :-0.3314 Min. :0.4993
## 1st Qu.:1.0000 1st Qu.: 1.0000 1st Qu.:0.7350
## Median :1.0000 Median : 1.0000 Median :0.9033
## Mean :0.9523 Mean : 0.8976 Mean :0.8353
## 3rd Qu.:1.0000 3rd Qu.: 1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. : 1.0000 Max. :1.0000
## 87405500 - pH 87405500 - solidos_totais 87405500 - E_coli
## Min. :0.3109 Min. :0.3797 Min. :1
## 1st Qu.:0.4284 1st Qu.:0.4477 1st Qu.:1
## Median :0.5052 Median :0.6036 Median :1
## Mean :0.6886 Mean :0.6652 Mean :1
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1
## Max. :1.0000 Max. :1.0000 Max. :1
## 87405500 - ANO_COLETA 87405500 - Condutividade 87406900 - DBO
## Min. :1 Min. :0.4527 Min. :0.5144
## 1st Qu.:1 1st Qu.:0.5737 1st Qu.:0.7408
## Median :1 Median :0.8099 Median :1.0000
## Mean :1 Mean :0.7836 Mean :0.8530
## 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1 Max. :1.0000 Max. :1.0000
## 87406900 - nitro_kjeldahl 87406900 - nitro_total 87406900 - fosforo_total
## Min. :-0.5366 Min. :0.3926 Min. :-0.3894
## 1st Qu.: 0.6958 1st Qu.:0.6754 1st Qu.: 0.6154
## Median : 0.9305 Median :0.8658 Median : 0.7260
## Mean : 0.7673 Mean :0.8084 Mean : 0.6906
## 3rd Qu.: 1.0000 3rd Qu.:1.0000 3rd Qu.: 1.0000
## Max. : 1.0000 Max. :1.0000 Max. : 1.0000
## 87406900 - temp_agua 87406900 - turbidez 87406900 - oxigenio_dissolvido
## Min. :-0.3840 Min. :-0.4238 Min. :-0.5366
## 1st Qu.: 0.4569 1st Qu.: 1.0000 1st Qu.: 1.0000
## Median : 0.6015 Median : 1.0000 Median : 1.0000
## Mean : 0.6624 Mean : 0.8905 Mean : 0.6685
## 3rd Qu.: 1.0000 3rd Qu.: 1.0000 3rd Qu.: 1.0000
## Max. : 1.0000 Max. : 1.0000 Max. : 1.0000
## 87406900 - nitro_amon 87406900 - pH 87406900 - solidos_totais
## Min. :0.4212 Min. :0.3926 Min. :0.4459
## 1st Qu.:0.7397 1st Qu.:1.0000 1st Qu.:0.6187
## Median :0.8658 Median :1.0000 Median :0.6958
## Mean :0.8237 Mean :0.8628 Mean :0.7839
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## 87406900 - E_coli 87406900 - ANO_COLETA 87406900 - Condutividade
## Min. :1 Min. :-0.4238 Min. :0.4024
## 1st Qu.:1 1st Qu.: 1.0000 1st Qu.:0.6399
## Median :1 Median : 1.0000 Median :0.8068
## Mean :1 Mean : 0.8905 Mean :0.7870
## 3rd Qu.:1 3rd Qu.: 1.0000 3rd Qu.:1.0000
## Max. :1 Max. : 1.0000 Max. :1.0000
## 87409900 - DBO 87409900 - nitro_kjeldahl 87409900 - nitro_total
## Min. :0.5624 Min. :0.6898 Min. :0.5245
## 1st Qu.:1.0000 1st Qu.:0.9999 1st Qu.:0.8314
## Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.9371 Mean :0.9603 Mean :0.8823
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## 87409900 - fosforo_total 87409900 - temp_agua 87409900 - turbidez
## Min. :0.4560 Min. :0.4309 Min. :0.4405
## 1st Qu.:0.8175 1st Qu.:1.0000 1st Qu.:1.0000
## Median :0.8565 Median :1.0000 Median :1.0000
## Mean :0.8426 Mean :0.9562 Mean :0.9570
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## 87409900 - oxigenio_dissolvido 87409900 - nitro_amon 87409900 - pH
## Min. :1 Min. :0.7569 Min. :0.4049
## 1st Qu.:1 1st Qu.:0.8915 1st Qu.:1.0000
## Median :1 Median :1.0000 Median :1.0000
## Mean :1 Mean :0.9482 Mean :0.9542
## 3rd Qu.:1 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1 Max. :1.0000 Max. :1.0000
## 87409900 - solidos_totais 87409900 - E_coli 87409900 - ANO_COLETA
## Min. :0.4405 Min. :0.4528 Min. :0.4528
## 1st Qu.:0.5429 1st Qu.:0.5245 1st Qu.:1.0000
## Median :1.0000 Median :1.0000 Median :1.0000
## Mean :0.8442 Mean :0.8215 Mean :0.9579
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## 87409900 - Condutividade
## Min. :0.4049
## 1st Qu.:0.4895
## Median :0.7254
## Mean :0.7294
## 3rd Qu.:1.0000
## Max. :1.0000parametros_IQA %>%
# group_by(CODIGO) %>%
select(
nitro_kjeldahl, Condutividade
) %>%
# correlation::cor_test() %>%
plot()
correlação-parametros-qualidade-agua-gravataí no período 1990-2020
str(
plot(
correlation::cor_test(
parametros_IQA,
"nitro_kjeldahl",
"Condutividade"
)
)
)## List of 9
## $ data : tibble [1,179 x 14] (S3: tbl_df/tbl/data.frame)
## ..$ CODIGO : chr [1:1179] "87398950" "87398900" "87405500" "87398950" ...
## ..$ pH : num [1:1179] 6.9 6.8 6.6 6.4 6.9 6.7 6.6 6.7 6.3 7.1 ...
## ..$ DBO : num [1:1179] 5 5 11 3 3 2 4 3 4 2 ...
## ..$ E_coli : num [1:1179] 4 40 12.8 10.4 32 16.8 40 6.4 10.4 18.4 ...
## ..$ nitro_amon : num [1:1179] NA NA NA NA NA NA NA NA NA NA ...
## ..$ nitro_kjeldahl : num [1:1179] 1.42 1.61 6.62 0.79 1.45 0.76 1.23 1.37 2.25 1.34 ...
## ..$ nitro_total : num [1:1179] 1.42 1.61 6.62 0.79 1.45 0.76 1.23 1.37 2.25 1.34 ...
## ..$ fosforo_total : num [1:1179] 0.113 0.0883 0.353 0.0908 0.118 0.0326 0.162 0.138 0.217 0.0879 ...
## ..$ temp_agua : num [1:1179] 25 31 31 20 28 16 16 16 18.5 28 ...
## ..$ turbidez : num [1:1179] 20 20 8.5 23 19 17 12 22 17 23 ...
## ..$ solidos_totais : num [1:1179] 121 112 160 93 127 75 80 201 84 123 ...
## ..$ oxigenio_dissolvido: num [1:1179] 6.5 8.7 5.3 7.8 7.6 8.5 7.3 6.9 6.7 6.3 ...
## ..$ Condutividade : num [1:1179] 90 47 147 43 NA 47 70 60.6 72.7 56 ...
## ..$ ANO_COLETA : num [1:1179] 1994 1994 1994 1993 1994 ...
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## train_panel_guides: function
## transform: function
## super: <ggproto object: Class CoordCartesian, Coord, gg>
## $ facet :Classes 'FacetNull', 'Facet', 'ggproto', 'gg' <ggproto object: Class FacetNull, Facet, gg>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map_data: function
## params: list
## setup_data: function
## setup_params: function
## shrink: TRUE
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetNull, Facet, gg>
## $ plot_env :<environment: 0x0000000028035560>
## $ labels :List of 4
## ..$ title : NULL
## ..$ subtitle: chr "r = 0.83, 95% CI [0.81, 0.86], t(605) = 36.90, p < .001"
## ..$ x : chr "nitro_kjeldahl"
## ..$ y : chr "Condutividade"
## - attr(*, "class")= chr [1:2] "gg" "ggplot"Time for this code chunk to run: 3.759840965271
8.1.2 Condutividade elétrica
(cond_elet <- ggplot(plan_wide_19902020,
aes(CODIGO,
Condutividade))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
facet_wrap(~periodo)+
labs(title = "Condutividade elétrica no período 1990-2020",
x="Estação",
y="µmhos/cm")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time for
this code chunk to run: 1.5030779838562
(cond_elet_p1 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
Condutividade))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Condutividade elétrica no período 1990-2000",
x="Estação",
y="µmhos/cm")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.624596118927002
(cond_elet_p2 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010"),
aes(CODIGO,
Condutividade))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Condutividade elétrica no período 2000-2010",
x="Estação",
y="µmhos/cm")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.729229927062988
(cond_elet_p3 <- ggplot(plan_wide_19902020 %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020"),
aes(CODIGO,
Condutividade))+
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=500,
ymax=Inf,
alpha=1,
fill="#eb5661")+ #classe 4
annotate("rect",
xmin=-Inf,
xmax=Inf,
ymin=-Inf,
ymax=500,
alpha=1,
fill="#8dcdeb")+ #classe 1
stat_boxplot(geom = 'errorbar',
width=0.3,
position = position_dodge(width = 0.65))+
geom_boxplot(fill='#F8F8FF',
color="black",
outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
width= 0.7)+
labs(title = "Condutividade elétrica no período 2010-2020",
x="Estação",
y="µmhos/cm")+
scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
n.breaks = 8,
limits = c(0,
max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
labels = scales::number_format(accuracy = 1,
decimal.mark = ",",
big.mark = " "))+
ggbeeswarm::geom_quasirandom(
size = 1.2,
alpha = .25,
width = .07,
)+
scale_x_discrete(limits = c("87398500",
"87398980",
"87398900",
"87398950",
"87405500",
"87406900",
"87409900"),
labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
)+
geom_smooth(method = "lm",
se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
aes(group=1),
alpha=.5,
na.rm = TRUE,
size = 1)+
theme_grafs()
)
Time
for this code chunk to run: 0.614361047744751
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
Time
for this code chunk to run: 1.75470209121704
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
select(CODIGO, Condutividade, ANO_COLETA) %>%
filter(ANO_COLETA>"1990" &
ANO_COLETA<="2000") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Condutividade,
na.rm = TRUE),
q1 =
quantile(Condutividade, 0.25,
na.rm = TRUE),
median =
median(Condutividade,
na.rm = TRUE),
mean =
mean(Condutividade,
na.rm= TRUE),
q3 =
quantile(Condutividade, 0.75,
na.rm = TRUE),
max =
max(Condutividade,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 9.4 51.1 67 75.1 83.2 340
## 2 87398900 10 41.5 51 55.3 64.2 160
## 3 87398950 9 41.5 51.5 60.1 69.5 160
## 4 87398980 11.3 42.4 52.0 53.0 67.0 83.8
## 5 87405500 25 68.7 88.2 130. 170 560
## 6 87406900 52 88.4 133. 193. 256. 576
## 7 87409900 29 80 110. 134. 168. 460(sum_cond_elet_p2 <- plan_wide_19902020 %>%
select(CODIGO, Condutividade, ANO_COLETA) %>%
filter(ANO_COLETA>"2000" &
ANO_COLETA<="2010") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Condutividade,
na.rm = TRUE),
q1 =
quantile(Condutividade, 0.25,
na.rm = TRUE),
median =
median(Condutividade,
na.rm = TRUE),
mean =
mean(Condutividade,
na.rm= TRUE),
q3 =
quantile(Condutividade, 0.75,
na.rm = TRUE),
max =
max(Condutividade,
na.rm = TRUE))
)## # A tibble: 7 x 7
## CODIGO min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 87398500 11.9 67.0 82.6 84.8 102. 164.
## 2 87398900 11 44.4 52.3 57.1 72.6 136.
## 3 87398950 39.8 58.4 76 82.3 98.3 160
## 4 87398980 9.4 42.4 49.7 51.5 62 114.
## 5 87405500 17 77.5 107 142. 171. 679
## 6 87406900 23.1 85.6 124. 164. 199. 619
## 7 87409900 56.1 114. 177 200. 242 454(sum_cond_elet_p3 <- plan_wide_19902020 %>%
select(CODIGO, Condutividade, ANO_COLETA) %>%
filter(ANO_COLETA>"2010" &
ANO_COLETA<="2020") %>%
group_by(CODIGO) %>%
summarize(
min =
min(Condutividade,
na.rm = TRUE),
q1 =
quantile(Condutividade, 0.25,
na.rm = TRUE),
median =
median(Condutividade,
na.rm = TRUE),
mean =
mean(Condutividade,
na.rm= TRUE),
q3 =
quantile(Condutividade, 0.75,
na.rm = TRUE),
max =
max(Condutividade,
na.rm = TRUE),
n =
length(Condutividade))
)## # A tibble: 7 x 8
## CODIGO min q1 median mean q3 max n
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500 0.01 68.5 80.2 80.4 99.5 125. 34
## 2 87398900 39.7 53.4 58.3 61.1 65.5 103 36
## 3 87398950 40.9 64.7 70.1 76.1 82.5 195. 35
## 4 87398980 43.2 51.7 54.0 56.3 61.0 78.9 28
## 5 87405500 47 85.8 121. 146. 209. 286 33
## 6 87406900 62.7 95.9 142. 163. 216. 354. 35
## 7 87409900 65.7 121. 159. 179. 245. 498. 37# plan_wide_19902020 %>%
# select(CODIGO, IQA) %>%
# group_by(CODIGO) %>%
# summarize(
# min =
# min(IQA,
# na.rm = TRUE),
# q1 =
# quantile(IQA, 0.25,
# na.rm = TRUE),
# median =
# median(IQA,
# na.rm = TRUE),
# mean =
# mean(IQA,
# na.rm= TRUE),
# q3 =
# quantile(IQA, 0.75,
# na.rm = TRUE),
# max =
# max(IQA,
# na.rm = TRUE))Time for this code chunk to run: 0.240110158920288
ggsave("cond_elet.png",
units = c("px"),
width = 4500,
height = 2993,
plot = cond_elet,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_p1.png",
plot = cond_elet_p1,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_p2.png",
plot = cond_elet_p2,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_p3.png",
plot = cond_elet_p3,
path = "./graficos",
dpi = 300,
type = "cairo")
ggsave("cond_elet_3periodos.png",
units = c("px"),
width = 4500,
height = 2993,
plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
path = "./graficos",
dpi = 300,
type = "cairo")Time
for this code chunk to run: 6.44113302230835
8.2 Textando o texto
- § falar do comportamento geral dos dados
- 2º § - xº § -> abordar os principais parâmetros que estão sendo impactados, detalhando, nas estações mais relevantes, como ficaram os quartis/mediana etc.
10.8
Os resultados apontam que para o parâmetro OD
---
title: "TCC"
author: "Leonardo Fernandes Wink"
date: "`r format(Sys.time(), '%d/%m/%Y')`"
output:
  html_document: 
    highlight: haddock
    keep_md: yes
    number_sections: yes
    theme: flatly
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: no
    fig_width: 10
    fig_height: 6.66
    fig_caption: yes
    code_download: true
  pdf_document:
    toc: yes
  word_document: 
    toc: yes
    keep_md: yes
  github_document:
    html_preview: true
always_allow_html: yes
editor_options: 
  chunk_output_type: console
fig.align: center
---

```{r Rotina pra toda vez que abrir o documento, echo = FALSE}
# Abrir o GitHub Desktop
# Verificar se há pull pra ser feito
# Abrir o RStudio
```

# Brief explanation

Every boxplot means a monitoring point (Ponto de monitoramento (or PM) in portuguese). My goal here is to analyze the evolution between decades of each water quality parameter that compounds the Water Quality Index (WQI).

The river flows in the east-west direction as shown in the image below.

![](images/paste-7AD7027F.png)

The logic behind the sorting in the boxplots is because of 2 main reasons:

1.  The original monitoring point isn't easy to understand (8 digits, like 87409900)
2.  Changing the original nomenclature to PM1, PM2 (...) makes it easier to understand that the last point has water contributions of every other point upstream.

Some features that I want to add:
- If the parameter is x, then use x's classes (with its own classes background color plotted)
- Define the timescale, should act just like a filter

```{r p1 example, eval=FALSE}
# plan_wide_19902020 %>%
#   filter(ANO_COLETA > "1990" &
#          ANO_COLETA <= "2000")
```

# Anotações de coisas por fazer:

-   Descobrir como colocar as estações no sentido correto montante -\> jusante nos sumários

> 87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900

-   ~~Aprender a segmentar o meu dataset por períodos~~
-   aprender a criar uma nova coluna com a segmentação dos períodos
-   maybe use `~facet.grid`
-   aprender a colocar a legenda dentro do gráfico
    -   reduzir o tamanho da legenda
-   ~~corrigir os valores 0 de IQA pra NA~~
-   descobrir como conseguir a equação do lm
-   ~~aprender a pivotar o sumário~~ -\> meu sumário do google docs ta batendo direitinho com o do R
-   descobrir se há outros TCCs com disponibilização de códigos
-   `Namon` tá com com casa decimal `","` e `ptot` tá com `"."`
-   correlação forte entre condutividade e Namon/Ptot/DBO

| 1990-2000 | 2000-2010 | 2010-2020 |
|:---------:|:---------:|:---------:|
| 1990-2000 | 2000-2010 | 2010-2020 |

# Instalar os pacotes

```{r instalar pacotes, eval=FALSE}
# install.packages(tidyverse)
```

## acessar os pacotes

```{r Acessar os pacotes, message = FALSE, warning = TRUE}
# library(readr)
# library(rmarkdown)
# # library(qboxplot)
# library(readxl)
# library(pillar)
# library(dplyr)
# library(tidyverse)
# library(gapminder)
# library(knitr)
# library(kableExtra)
# library(ggpubr)
# library(gridExtra)
# library(modelsummary)
# library(gtsummary)
# library(GGally)
pacman::p_load(readr, rmarkdown, readxl,
               pillar, dplyr, tidyverse,
               gapminder, knitr, kableExtra,
               gridExtra, #modelsummary, 
               gtsummary, ggplot2,
               ggbeeswarm, GGally,
               report)
# pacman::p_load(tibbletime)
cite_packages()
```

```{r cronometrando quanto tempo cada chunk leva}
knitr::knit_hooks$set(time_it = local({
   now <- NULL
   function(before, options) {
      if (before) {
         # record the current time before each chunk
         now <<- Sys.time()
      } else {
         # calculate the time difference after a chunk
         res <- difftime(Sys.time(), now)
         # return a character string to show the time
         paste("Time for this code chunk to run:", res)
      }
   }
}))

knitr::opts_chunk$set(time_it = TRUE)
```

```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE)
```

### referenciando os pacotes
```{r referenciando os pacotes}
version$version.string
citation(package = "tidyverse")
```


## importando a planilha

```{r Importando a planilha, echo = FALSE, message = TRUE, warning = FALSE}
plan_wide_19902020 <- read_delim("https://raw.githubusercontent.com/leonardofwink/TCC_gh/main/plan_wide_19902020.tsv",
                                 delim = "\t", 
                                 escape_double = FALSE,
                                 col_types = cols(
                                   Alcalinidade = col_double(),
                                   CODIGO = col_character(), 
                                   COORD_GEO_LAT_GRAU = col_double(),
                                   COORD_GEO_LONG_GRAU = col_double(),
                                   DATA_COLETA = col_date(format = "%d/%m/%Y"),
                                   Nitrato = col_double(), 
                                   Nitrito = col_double(),
                                   SDT = col_double(), 
                                   SST = col_double(),
                                   `Vazao` = col_double(), 
                                   `Vazao rio` = col_double()
                                 ),
                                 locale = locale(
                                   date_names = "pt", 
                                   decimal_mark = ",",
                                   grouping_mark = ""
                                 ),
                                 trim_ws = TRUE
) %>% 
  rename(
    E_coli = `Escherichia coli`,
    fosfato_orto = `Fosfato orto`,
    fosforo_total = `Fósforo total`,
    nitro_organico = `Nitrogênio orgânico`,
    nitro_amon = `Nitrogênio amoniacal`,
    nitro_kjeldahl = `Nitrogênio Kjeldahl`,
    nitro_total = `Nitrogênio total`,
    oxigenio_dissolvido = `Oxigênio dissolvido`,
    sat_OD = `%sat OD`,
    temp_agua = `Temperatura água`,
    temp_ar = `Temperatura ar`,
    transparencia_agua = `Transparência água`,
    vazao_rio = `Vazao rio`,
    coliformes_termo = `Coliformes termotol`,
    turbidez = Turbidez,
    solidos_totais = `Sólidos totais`,
  )

glimpse(plan_wide_19902020)
# teste <- plan_wide_19902020 %>%
#   dplyr::filter(DATA_COLETA >= as.POSIXct("2010-01-01")) #this works
# 
# teste$DATA_COLETA <- as.POSIXct(teste$DATA_COLETA)
# 
# teste %>% 
#   dplyr::arrange(DATA_COLETA)
# teste %>% 
#   filter_time(time_formula = '2013-01-01' ~ '2020-12-31')
# 
# 
# typeof(teste$DATA_COLETA)
# 
#   as_tbl_time(plan_wide_19902020, index = DATA_COLETA)
# str(plan_wide_19902020$DATA_COLETA)
```

```{r Visualização da planilha importada, echo = FALSE}
paged_table(plan_wide_19902020,
            options = list(rows.print = 15,
                           cols.print = 10))
```

# data wrangling

```{r data wrangling}
# Como há dados faltantes, no cálculo entre o produto das colunas, ele acaba interpretando como se fosse zero, mas na verdade é NA
plan_wide_19902020 <- plan_wide_19902020 %>% 
   mutate(IQA = ifelse(IQA == 0, NA, IQA))

parametros_IQA <- plan_wide_19902020 %>%
  select(CODIGO,
         pH,
         DBO,
         E_coli,
         nitro_amon,
         nitro_kjeldahl,
         nitro_total,
         fosforo_total,
         temp_agua,
         turbidez,
         solidos_totais,
         oxigenio_dissolvido,
         Condutividade,
         ANO_COLETA)

write.csv(parametros_IQA,
          "./parametros_IQA.csv",
          row.names = FALSE)

plan_wide_19902020 %>% 
  select(starts_with("IQA_^")) %>% 
  mutate(
    TESTANDOIQA = prod()
  )
# library(performance)
# modelo <- plan_wide_19902020 %>% 
#   select(CODIGO, oxigenio_dissolvido, periodo) %>% 
#   group_by(CODIGO, periodo) %>% 
#   lm() %>% 
#   performance::check_distribution()
# # lm()
# 
# check_model(modelo)
# performance::check_autocorrelation(modelo)

```

```{r Códigos Git, echo = FALSE}
# cd myrepo
# ls
# head README.md
# git status
# git add README.md
# git commit -m "A commit from my local computer"
# 
# cd .. # voltar pro diretório acima
# rm -rf myrepo/ #remover/apagar a pasta myrepo
```

```{r Aprendendo Git, echo = FALSE}
# slides da bia que ajudam mt
# https://beatrizmilz.github.io/slidesR/git_rstudio/11-2021-ENCE.html#20
# aprendendo a sincronizar usando esse guia -> 
# https://happygitwithr-com.translate.goog/push-pull-github.html?_x_tr_sl=auto&_x_tr_tl=pt&_x_tr_hl=pt-BR
# library(usethis)
# usethis::create_github_token() criar um código pra acesso e sincronização between R e github

# gitcreds::gitcreds_set() 
# 
# use_git_config(user.name = "leonardofwink",
#                user.email = "leonardofwink@gmail.com")
# usethis::gh_token_help()

# Como mostrar os dados de um arquivo via Git/GitHub
# git clone https://github.com/leonardofwink/myrepo.git
# cd myrepo #acessa a pasta myrepo
# ls #lista os arquivos da pasta 
# head README.md #mostra as primeiras observações do arquivo

# Como mostrar os dados de um arquivo via R
# head(C:/Users/Léo/myrepo/README.md)

# Adicionar uma linha ao README.md e verificar se o Git percebe a mudança
# echo "A line I wrote on my local computer" >> README.md
# git status
## C:\Users\Léo\myrepo>git status
## On branch main
## Your branch is up to date with 'origin/main'.
## 
## Changes not staged for commit:
##   (use "git add <file>..." to update what will be committed)
##   (use "git restore <file>..." to discard changes in working directory)
##         **modified:   README.md**
## 
## no changes added to commit (use "git add" and/or "git commit -a")
```

# setting theme

```{r setting theme}
theme_grafs <- function(bg = "white", 
                        coloracao_letra = "black") {
  theme(
    plot.title = 
      element_text(
        hjust = 0.5,
        color = coloracao_letra,
        size = 19),
    
    axis.title.x = 
      # element_text(
      # color = coloracao_letra,
      # size = 15,
      # angle = 0,),
      element_blank(),
    axis.title.y = element_text(
      color = coloracao_letra,
      size = 15,
      angle = 90),
    
    axis.text.x = element_text(
      color = coloracao_letra,
      size = 17),
    axis.text.y = element_text(
      color = coloracao_letra,
      size = 17,
      angle = 0),
    
    strip.background = element_rect(fill = bg,
                                    linetype = 1,
                                    size = 0.5,
                                    color = "black"),
    strip.text = element_text(size = 17),
    panel.background = element_rect(fill = bg),
    plot.background = element_rect(fill = bg),
    plot.margin = margin(l = 5, r = 10,
                         b = 5, t = 5)
  )
}
```

# setting different timescales

```{r setting periodos, echo = FALSE}
plan_wide_19902020 <- plan_wide_19902020 %>% 
  # select(CODIGO, ANO_COLETA) %>% 
  mutate(
    periodo = if_else(
      ANO_COLETA <= 2000, 
      "1990-2000",
      if_else(
        ANO_COLETA <= 2010,
        "2000-2010",
        "2010-2020"
      )
    )
  )
```

# setting sumaries

```{r Sumários, echo = FALSE}
# plan_wide_19902020 %>%
#   as_tibble() %>% 
#   filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000") %>% 
#   select(colnames(parametros_IQA)) %>% 
#   group_by(CODIGO) %>% 
#   group_by(colnames(parametros_IQA)) %>% 
#   summarise_each(
#     funs( 
#       min = 
#         min(., 
#             na.rm = TRUE),
#       q1 = 
#         quantile(., 0.25, 
#                  na.rm = TRUE),
#       median = 
#         median(., 
#                na.rm = TRUE),
#       mean = 
#         mean(., 
#              na.rm= TRUE),
#       q3 = 
#         quantile(., 0.75, 
#                  na.rm = TRUE),
#       max = 
#         max(., 
#             na.rm = TRUE),
#       n = 
#         length(.)
#     )
#   ) %>% 
#   pivot_longer(
#        !CODIGO,
#        names_to = "parametro",
#        values_to = "valor"
#     ) %>% 
#     pivot_wider(names_from = CODIGO,
#                 values_from = valor) %>% 
#   group_by(parametro)



# p2 <- plan_wide_19902020 %>%
#   filter(ANO_COLETA > "2000" &
#          ANO_COLETA <= "2010")
# 
# p3 <- plan_wide_19902020 %>%
#   filter(ANO_COLETA > "2010" &
#          ANO_COLETA <= "2020")

# periodo = c(p1 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000"),
# 
# p2 <- plan_wide_19902020 %>%
#   filter(ANO_COLETA > "2000" &
#            ANO_COLETA <= "2010"),
# 
# p3 <- plan_wide_19902020 %>%
#   filter(ANO_COLETA > "2010" &
#            ANO_COLETA <= "2020"))

# sumario <- function(parametros = parametros, periodo){
#   plan_wide_19902020 %>%
#    select(CODIGO, ., ANO_COLETA) %>% 
#    # filter(ANO_COLETA>"1990" &
#    #          ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(parametros, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(parametros, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(parametros, 
#               na.rm = TRUE),
#      mean = 
#        mean(parametros, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(parametros, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(parametros, 
#            na.rm = TRUE))
# }

# plan_wide_19902020 %>% 
#   sumario(parametros = DBO)

# sum_IQA_p1 <- plan_wide_19902020 %>%
#    select(CODIGO, IQA, ANO_COLETA) %>% 
#    filter(ANO_COLETA>"1990" &
#             ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(IQA, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(IQA, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(IQA, 
#               na.rm = TRUE),
#      mean = 
#        mean(IQA, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(IQA, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(IQA, 
#            na.rm = TRUE))
```

# Parâmetros físico-químicos

### Oxigênio Dissolvido

```{r Gráfico OD facetted, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2020"}
(od <- plan_wide_19902020 %>% 
    ggplot(
      aes(
        x = CODIGO,
        y = oxigenio_dissolvido,
        # color = periodo,
        # fill = periodo
        )
    )+
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = -Inf, ymax = 2,
             alpha = 1,
             fill = "#ac5079")+ #>pior classe
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 2, ymax = 4,
             alpha = 1,
             fill = "#eb5661")+ #classe 4
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 4, ymax = 5,
             alpha = 1,
             fill = "#fcf7ab")+ #classe 3
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin = 5, ymax = 6,
             alpha = 1,
             fill = "#70c18c")+ #classe 2
    annotate("rect",
             xmin = -Inf, xmax = Inf,
             ymin= 6, ymax = Inf,
             alpha = 1,
             fill = "#8dcdeb")+ #classe 1
    stat_boxplot(
      geom = 'errorbar',
      width = 0.3,
      position = position_dodge(width = 0.65)
    )+
    geom_boxplot(
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
      width = 0.7
    )+
    facet_wrap(~periodo)+
    labs(
      title = "Oxigênio Dissolvido no período 1990-2020",
      x= NULL,
      y="mg/L"
    )+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(
      expand = expansion(mult = c(0,0)),
      n.breaks = 11,
      limits = c(-0.3,21)
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    # scale_fill_brewer(palette = "Set1")+
    scale_color_manual(name = "Período",
                       breaks = c("p1", "p2", "p3"),
                       values = c("black", "#303030", "#696969"),
                       labels = c("1990-2000", "2000-2010", "2010-2020")
    )+
    geom_smooth(
      method = "lm",
      se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
      aes(group = 1),
      alpha = .5,
      na.rm = TRUE,
      size = 1
    )+
  theme_grafs()
)
```


```{r Gráfico OD periodo 1, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2000"}
(od_p1 <- ggplot(plan_wide_19902020 %>% 
                    filter(ANO_COLETA > "1990" &
                              ANO_COLETA <= "2000"),
                 aes(CODIGO,
                     oxigenio_dissolvido)
)+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = -Inf, ymax = 2,
            alpha = 1,
            fill = "#ac5079")+ #>pior classe
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 2, ymax = 4,
            alpha = 1,
            fill = "#eb5661")+ #classe 4
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 4, ymax = 5,
            alpha = 1,
            fill = "#fcf7ab")+ #classe 3
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 5, ymax = 6,
            alpha = 1,
            fill = "#70c18c")+ #classe 2
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin= 6, ymax = Inf,
            alpha = 1,
            fill = "#8dcdeb")+ #classe 1
   stat_boxplot(
      geom = 'errorbar',
      width = 0.3,
      position = position_dodge(width = 0.65)
   )+
   geom_boxplot(
      fill = '#F8F8FF',
      color = "black",
      outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
      width = 0.7
   )+
   labs(
      title = "Oxigênio Dissolvido no período 1990-2000",
      x="Estação",
      y="mg/L"
   )+
   ggbeeswarm::geom_quasirandom(
      size = 1.2,
      alpha = .25,
      width = .07,
   )+
   scale_y_continuous(
      expand = expansion(mult = c(0,0)),
      n.breaks = 11,
      limits = c(-1,21)
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(
      method = "lm",
      se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
      aes(group = 1),
      alpha = .5,
      na.rm = TRUE,
      size = 1
   )+
   theme_grafs()
)
```


```{r Gráfico OD periodo 2, echo = FALSE, warning=FALSE, message = FALSE}
(od_p2 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2000" &
                             ANO_COLETA<="2010"),
                aes(CODIGO,
                    oxigenio_dissolvido))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=2,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=2,
             ymax=4,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=4,
             ymax=5,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=6,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=6,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Oxigênio Dissolvido no período 2000-2010",
         x="Estação",
         y=NULL)+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(
       expand = expansion(mult = c(0,0)),
       n.breaks = 11,
       limits = c(-1,21))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```


```{r Gráfico OD periodo 3, echo = FALSE, warning=FALSE, message = FALSE}
(od_p3 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2010" &
                             ANO_COLETA<="2020") %>% 
                  group_by(CODIGO),
                aes(CODIGO,
                    oxigenio_dissolvido))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=2,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=2,
             ymax=4,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=4,
             ymax=5,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=6,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=6,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
   geom_boxplot(
     # aes(
     #   x = oxigenio_dissolvido,
     #   ymin = min(oxigenio_dissolvido),
     #   lower = quantile(oxigenio_dissolvido, 0.30, na.rm = TRUE),
     #   middle = median(oxigenio_dissolvido),
     #   upper = quantile(oxigenio_dissolvido, 0.80, na.rm = TRUE),
     #   ymax = max(oxigenio_dissolvido)
     # ),
     # stat = "identity",
     fill='#F8F8FF',
     color="black",
     outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
     width= 0.7
   )+
   labs(title = "Oxigênio Dissolvido no período 2010-2020",
        x=NULL,
        y=NULL)+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(
       expand = expansion(mult = c(0,0)),
       n.breaks = 11,
       limits = c(-1,21))+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```

```{r Gráfico OD 3 periodos juntos, echo = TRUE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2020"}
grid.arrange(od_p1, od_p2, od_p3, ncol = 3)
```

```{r Salvando OD, warning=FALSE, message = FALSE,}
ggsave("od.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = od,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p1.png",
       plot = od_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p2.png",
       plot = od_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p3.png",
       plot = od_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(od_p1, od_p2, od_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

```{r Gráfico OD_chernobyl, echo = FALSE, warning=FALSE, message = FALSE}
# p1 <- function(plan_wide_19902020, ANO_COLETA) {
#   plan_wide_19902020 %>% 
#     filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000")
# }
# 
# 
# classes_od <- function(plan_wide_19902020, parametro, periodo){
#   ggplot(plan_wide_19902020 %>%
#            periodo),
#   aes(CODIGO,
#       parametro)
# }


# (od_chernobyl <- ggplot(plan_wide_19902020 %>%
#                           p1(ANO_COLETA > "1990" &
#                                ANO_COLETA <= "2000"),
#                         aes(CODIGO,
#                             oxigenio_dissolvido))+
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=-Inf,
#              ymax=2,
#              alpha=1,
#              fill="#ac5079")+ #>pior classe
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=2,
#              ymax=4,
#              alpha=1,
#              fill="#eb5661")+ #classe 4
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=4,
#              ymax=5,
#              alpha=1,
#              fill="#fcf7ab")+ #classe 3
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=5,
#              ymax=6,
#              alpha=1,
#              fill="#70c18c")+ #classe 2
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=6,
#              ymax=Inf,
#              alpha=1,
#              fill="#8dcdeb")+ #classe 1
#     stat_boxplot(geom = 'errorbar',
#                  width=0.3,
#                  position = position_dodge(width = 0.65))+
#     geom_boxplot(fill='#F8F8FF',
#                  color="black",
#                  outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
#                  width= 0.7)+
#     labs(title = "Oxigênio Dissolvido no período 1990-2000",
#          x="Estação",
#          y="mg/L")+
#     # geom_jitter(width = .07,
#     #             alpha=.15,
#     #             size=1.,
#     #             color="black")+
#     ggbeeswarm::geom_quasirandom(
#       size = 1.2,
#       alpha = .25,
#       width = .07,
#     )+
#     scale_y_continuous(expand = expansion(mult = c(0,0)),
#                        n.breaks = 11,
#                        limits = c(-1,21))+
#     scale_x_discrete(limits = c("87398500",
#                                 "87398980",
#                                 "87398900",
#                                 "87398950",
#                                 "87405500",
#                                 "87406900",
#                                 "87409900"),
#                      labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
#     )+
#     geom_smooth(method = "lm",
#                 se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                 aes(group=1),
#                 alpha=.5,
#                 na.rm = TRUE,
#                 size = 1)+
#     # geom_line(
#     #   aes(color="red"),
#     #   alpha=.0)+
#     # scale_color_manual("Legenda",
#     #                    guide="legend",
#     #                    values = c("Classe 1"="#8dcdeb",
#     #                               "Classe 2"="#70c18c",
#     #                               "Classe 3"="#fcf7ab",
#     #                               "Classe 4"="#eb5661",
#     #                               "Pior Classe"="#ac5079"))+
#     # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#   #                                               lwd=c(2,2,2,2,2),
#   #                                               shape=c(NA,NA,NA,NA,NA),
#   #                                               alpha=1)))+
#   theme(
#     plot.title = element_text(size = 19),
#     axis.title.y = element_text(size = 15),
#     axis.text.y = element_text(size = 17),
#     axis.text.x = element_text(size = 17),
#   )
# )
```

```{r Gráfico IQA OD periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqaod_p1 <-ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA > "1990" &
                                ANO_COLETA <= "2000"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 1990-2000",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```

```{r Gráfico IQA OD periodo2, echo = FALSE, warning= FALSE, message = FALSE}
(iqaod_p2 <-ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA > "2000" &
                                ANO_COLETA <= "2010"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2000-2010",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)

```

```{r Gráfico IQA OD periodo3, echo = FALSE, warning=FALSE, message = FALSE}
(iqaod_p3 <-ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA > "2010" &
                                ANO_COLETA <= "2020"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2010-2020",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(
       method = "lm",
       se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
       aes(group=1),
       alpha=.5,
       na.rm = TRUE,
       size = 1
    )+
    theme_grafs()
)
```

```{r Gráfico OD_IQA 6 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqaod_p1, iqaod_p2, iqaod_p3, ncol = 3)
```

```{r Sumário OD, echo = FALSE, warning=FALSE, message = FALSE,}
(sum_od_p1 <- plan_wide_19902020 %>%
    select(CODIGO, oxigenio_dissolvido, ANO_COLETA) %>% 
    filter(ANO_COLETA>"1990" &
             ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   # CODIGO == "87398500" <- "teste1"
    # %>% 
 summarize(
       max = 
          max(oxigenio_dissolvido, na.rm = TRUE),
       q3 = 
          quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
       median = 
          median(oxigenio_dissolvido, na.rm = TRUE),
       mean = 
          mean(oxigenio_dissolvido, na.rm= TRUE),
       q1 = 
          quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
       min = 
          min(oxigenio_dissolvido, na.rm = TRUE),
       n = 
          length(oxigenio_dissolvido)
    ) %>% 
    pivot_longer(
       !CODIGO,
       names_to = "par",
       values_to = "valor"
    ) %>% 
    pivot_wider(names_from = CODIGO,
                values_from = valor) %>% 
   rename(
     "PM1" = "87398500",
     "PM2" = "87398900",
     "PM3" = "87398950",
     "PM4" = "87398980",
     "PM5" = "87405500",
     "PM6" = "87406900",
     "PM7" = "87409900"
   ) 
 )


# teste1 <- parametros_IQA %>% 
#   group_by(CODIGO) %>% 
#   pivot_longer(
#     !CODIGO,
#     names_to = "parametro",
#     values_to = "valor"
#   ) %>% 
#   # group_by(parametro)
#   pivot_wider(
#     names_from = CODIGO,
#     values_from = valor,
#     # .groups = "drop"
#   ) %>% 
#   rename(
#     "PM1" = "87398500",
#     "PM2" = "87398900",
#     "PM3" = "87398950",
#     "PM4" = "87398980",
#     "PM5" = "87405500",
#     "PM6" = "87406900",
#     "PM7" = "87409900"
#   ) %>%
#   select(par, PM1, PM2, PM3, PM4, PM5, PM6, PM7) %>% 
#   filter(
#     par == "pH"
#   ) 
# %>% 
#   unnest(dplyr::everything())


# teste1$PM1[2]

# %>%
#   summarize(
#     max =
#       max(oxigenio_dissolvido, na.rm = TRUE),
#     q3 =
#       quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
#     median =
#       median(oxigenio_dissolvido, na.rm = TRUE),
#     mean =
#       mean(oxigenio_dissolvido, na.rm= TRUE),
#     q1 =
#       quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
#     min =
#       min(oxigenio_dissolvido, na.rm = TRUE),
#     n =
#       length(oxigenio_dissolvido)
#     )
# #     
# 
# sum(sum_od_p1$n)



(sum_od_p2 <- plan_wide_19902020 %>%
      select(CODIGO, oxigenio_dissolvido, ANO_COLETA) %>% 
      filter(ANO_COLETA>"2000" &
                ANO_COLETA<="2010") %>% 
      group_by(CODIGO) %>% 
      summarize(
         min = 
            min(oxigenio_dissolvido, na.rm = TRUE),
         q1 = 
            quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
         median = 
            median(oxigenio_dissolvido, na.rm = TRUE),
         mean = 
            mean(oxigenio_dissolvido, na.rm= TRUE),
         q3 = 
            quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
         max = 
            max(oxigenio_dissolvido, na.rm = TRUE)
      )
)

(sum_od_p3 <- plan_wide_19902020 %>%
      select(CODIGO, oxigenio_dissolvido, ANO_COLETA) %>% 
      filter(ANO_COLETA>"2010" &
                ANO_COLETA<="2020") %>% 
      group_by(CODIGO) %>% 
      summarize(
         min = 
            min(oxigenio_dissolvido, na.rm = TRUE),
         q1 = 
            quantile(oxigenio_dissolvido, 0.25, na.rm = TRUE),
         median = 
            median(oxigenio_dissolvido, na.rm = TRUE),
         mean = 
            mean(oxigenio_dissolvido, na.rm= TRUE),
         q3 = 
            quantile(oxigenio_dissolvido, 0.75, na.rm = TRUE),
         max = 
            max(oxigenio_dissolvido, na.rm = TRUE)
      )
)

#   pivot_wider(id_cols = CODIGO,
#               names_from = CODIGO,
#               values_from = oxigenio_dissolvido)
# 
# 
#   group_by(CODIGO) %>%
#   get_summary_stats(type = "common") %>%
#   pivot_wider(id_cols = variable,
#               names_from = CODIGO,
#               values_from = variable$oxigenio_dissolvido)
# 
# # install.packages("ggpubr")
# # library(ggpubr)
```


### Demanda Bioquímica de Oxigênio

```{r Gráfico DBO facetted, fig.cap="Demanda Bioquímica de Oxigênio no período 1990-2020"}
(dbo <- ggplot(plan_wide_19902020,
               aes(x = CODIGO,
                   y = DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   facet_wrap(~periodo)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico DBO período1, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p1<-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"1990" &
                             ANO_COLETA<="2000"),
                aes(CODIGO,
                    DBO))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3,
             ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2000",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico DBO período2, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p2<-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2000" &
                             ANO_COLETA<="2010"),
                aes(CODIGO,
                    DBO))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico DBO período3, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p3<-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2010" &
                             ANO_COLETA<="2020"),
                aes(CODIGO,
                    DBO, na.rm=TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=10,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=5,
             ymax=10,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3,
             ymax=5,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Demanda Bioquímica de Oxigênio no período 2010-2020",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(1,100),
                       trans = "log10")+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
        geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo1, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo1<-ggplot(plan_wide_19902020 %>% 
                    filter(ANO_COLETA>"1990" &
                             ANO_COLETA<="2000"),
                  aes(CODIGO,
                      IQA_DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 1990-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo2, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo2<-ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                  aes(CODIGO,
                      IQA_DBO))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1))
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Variação do IQA para o parâmetro DBO 2000-2010",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico IQA DBO periodo3, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo3<-ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2010" &
                               ANO_COLETA<="2020"),
                  aes(CODIGO,
                      IQA_DBO))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1))
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Variação do IQA para o parâmetro DBO 2010-2020",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
        geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico DBO 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3)
```

```{r Sumário DBO, warning=FALSE, message = FALSE,}
(sum_dbo_p1 <- plan_wide_19902020 %>%
   select(CODIGO, DBO, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(DBO, 
           na.rm = TRUE),
     q1 = 
       quantile(DBO, 0.25, 
                na.rm = TRUE),
     median = 
       median(DBO, 
              na.rm = TRUE),
     mean = 
       mean(DBO, 
            na.rm= TRUE),
     q3 = 
       quantile(DBO, 0.75, 
                na.rm = TRUE),
     max = 
       max(DBO, 
           na.rm = TRUE))
)

(sum_dbo_p2 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)

(sum_dbo_p3 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)
```

```{r Salvando DBO, warning=FALSE, message = FALSE,}
ggsave("dbo.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = dbo,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p1.png",
       plot = dbo_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p2.png",
       plot = dbo_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p3.png",
       plot = dbo_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Fósforo total

```{r Gráfico fósforo total facetted, fig.cap="Fósforo total no período 1990-2020"}
(ptot <- ggplot(plan_wide_19902020,
                aes(CODIGO,
                    fosforo_total))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
  facet_wrap(~periodo)+
    labs(title = "Fósforo total no período 1990-2020",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Fósforo total periodo1, warning = FALSE, message = FALSE}
(ptot_p1<-ggplot(plan_wide_19902020%>% 
                   filter(ANO_COLETA>"1990" &
                             ANO_COLETA<="2000"),
                 aes(CODIGO,
                     fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 1990-2000",
         x="Estação",
         y="mg/L")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Gráfico Fósforo total periodo2, warning = FALSE, message = FALSE}
(ptot_p2 <- ggplot(plan_wide_19902020%>% 
                      filter(ANO_COLETA>"2000" &
                                ANO_COLETA<="2010"),
                   aes(CODIGO,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2000-2010",
         x="Estação",
         y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                 max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                      trans = "log10")+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Gráfico Fósforo total periodo3, warning = FALSE, message = FALSE}
(ptot_p3 <- ggplot(plan_wide_19902020%>% 
                      filter(ANO_COLETA>"2010" &
                                ANO_COLETA<="2020"),
                   aes(CODIGO,
                       fosforo_total))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.15,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0.1,
             ymax=0.15,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=0.1,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Fósforo total no período 2010-2020",
         x="Estação",
         y="mg/L")+
    scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                       n.breaks = 8,
                       limits = c(min(plan_wide_19902020$fosforo_total, na.rm = TRUE),
                                  max(plan_wide_19902020$fosforo_total), na.rm = TRUE),
                       trans = "log10")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)

```

```{r Gráfico Ptot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3)
```

```{r Sumário Fósforo total, warning=FALSE, message = FALSE,}
(sum_ptot_p1 <- plan_wide_19902020 %>%
    select(CODIGO, fosforo_total, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(fosforo_total, na.rm = TRUE),
     q1 = 
       quantile(fosforo_total, 0.25, na.rm = TRUE),
     median = 
       median(fosforo_total, na.rm = TRUE),
     mean = 
       mean(fosforo_total, na.rm= TRUE),
     q3 = 
       quantile(fosforo_total, 0.75, na.rm = TRUE),
     max = 
       max(fosforo_total, na.rm = TRUE)))

(sum_ptot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, fosforo_total, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))

(sum_ptot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, fosforo_total, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(fosforo_total, na.rm = TRUE),
      q1 = 
        quantile(fosforo_total, 0.25, na.rm = TRUE),
      median = 
        median(fosforo_total, na.rm = TRUE),
      mean = 
        mean(fosforo_total, na.rm= TRUE),
      q3 = 
        quantile(fosforo_total, 0.75, na.rm = TRUE),
      max = 
        max(fosforo_total, na.rm = TRUE)))

```

```{r Salvando Ptot, warning=FALSE, message = FALSE,}
ggsave("ptot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = ptot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p1.png",
       plot = ptot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p2.png",
       plot = ptot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p3.png",
       plot = ptot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Escherichia coli

```{r Gráfico Ecoli facetted, fig.cap="Escherichia-coli-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
ecoli__class <- function() {
  list(annotate("rect",
                xmin=-Inf,
                xmax=Inf,
                ymin=3200,
                ymax=Inf,
                alpha=1,
                fill="#ac5079")+ #>pior classe
         annotate("rect",
                  xmin=-Inf,
                  xmax=Inf,
                  ymin=800,
                  ymax=3200,
                  alpha=1,
                  fill="#fcf7ab")+ #classe 3
         annotate("rect",
                  xmin=-Inf,
                  xmax=Inf,
                  ymin=160,
                  ymax=800,
                  alpha=1,
                  fill="#70c18c")+ #classe 2
         annotate("rect",
                  xmin=-Inf,
                  xmax=Inf,
                  ymin=0,
                  ymax=160,
                  alpha=1,
                  fill="#8dcdeb") #classe 1
  )
}
  
(ecoli <- ggplot(plan_wide_19902020,
                 aes(CODIGO,
                     E_coli))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   facet_wrap(~periodo)+
   labs(title = "Escherichia coli no período 1990-2020",
        x="Estação",
        y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      # n.breaks = 9,
                      n.breaks = 6,
                      limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$E_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()+
    theme(
        axis.text.y = element_text(
          angle = 90, 
          # size=15,
          # face=2
        )
    )
)
```

```{r Gráfico Ecoli periodo1, warning = FALSE, message = FALSE}
(ecoli_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"1990" &
                                 ANO_COLETA<="2000"),
                    aes(CODIGO,
                        E_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 1990-2000",
         x="Estação",
         y="NMP/100mL")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
                                 max(plan_wide_19902020$E_coli, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Ecoli periodo2, warning = FALSE, message = FALSE}
(ecoli_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2000" &
                                 ANO_COLETA<="2010"),
                    aes(CODIGO,
                        E_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2000-2010",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$E_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico Ecoli periodo3, warning = FALSE, message = FALSE}
(ecoli_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2010" &
                                 ANO_COLETA<="2020"),
                    aes(CODIGO,
                        E_coli))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3200,
             ymax=Inf,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=800,
             ymax=3200,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=160,
             ymax=800,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=160,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Escherichia coli no período 2010-2020",
         x="Estação",
         y="NMP/100mL")+
    scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                       n.breaks = 9,
                       limits = c(min(plan_wide_19902020$E_coli, na.rm = TRUE),
                                  max(plan_wide_19902020$E_coli, na.rm = TRUE)),
                       trans = "log10",
                       labels = scales::number_format(accuracy = 1,
                                                      decimal.mark = ",",
                                                      big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico ecoli 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3)
```

```{r Sumário Ecoli, warning=FALSE, message = FALSE,}
(sum_ecoli_p1 <- plan_wide_19902020 %>%
    select(CODIGO, E_coli, ANO_COLETA) %>% 
    filter(ANO_COLETA>"1990" &
              ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(E_coli, 
           na.rm = TRUE),
     q1 = 
       quantile(E_coli, 0.25, 
                na.rm = TRUE),
     median = 
       median(E_coli, 
              na.rm = TRUE),
     mean = 
       mean(E_coli, 
            na.rm= TRUE),
     q3 = 
       quantile(E_coli, 0.75, 
                na.rm = TRUE),
     max = 
       max(E_coli, 
           na.rm = TRUE))
)

(sum_ecoli_p2 <- plan_wide_19902020 %>%
    select(CODIGO, E_coli, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(E_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(E_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(E_coli, 
               na.rm = TRUE),
      mean = 
        mean(E_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(E_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(E_coli, 
            na.rm = TRUE))
)

(sum_ecoli_p3 <- plan_wide_19902020 %>%
    select(CODIGO, E_coli, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(E_coli, 
            na.rm = TRUE),
      q1 = 
        quantile(E_coli, 0.25, 
                 na.rm = TRUE),
      median = 
        median(E_coli, 
               na.rm = TRUE),
      mean = 
        mean(E_coli, 
             na.rm= TRUE),
      q3 = 
        quantile(E_coli, 0.75, 
                 na.rm = TRUE),
      max = 
        max(E_coli, 
            na.rm = TRUE))
)
```

```{r Salvando ecoli, warning=FALSE, message = FALSE,}
ggsave("ecoli.png",
       plot = ecoli,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p1.png",
       plot = ecoli_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p2.png",
       plot = ecoli_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p3.png",
       plot = ecoli_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Nitrogênio amoniacal

```{r Gráfico Nitrogênio total facetted, fig.cap="nitrogenio-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(namon <- ggplot(plan_wide_19902020,
                 aes(CODIGO,
                     nitro_amon))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   facet_wrap(~periodo)+
   labs(title = "Nitrogênio amoniacal no período 1990-2020",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
                                 max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio line, warning = FALSE, message = FALSE}
periodo_inicial <- as.Date("1990-01-01", "%Y-%m-%d")
periodo_final <- as.Date("2021-01-01",  "%Y-%m-%d")

(nitro_line <- 
    
    plan_wide_19902020 %>%
    filter(ANO_COLETA > "1990" &
             ANO_COLETA <= "2020") %>%
    select(CODIGO, nitro_amon, DATA_COLETA, periodo) %>%
    group_by(CODIGO) %>%
    # pivot_wider(
    #   names_from = CODIGO,
    #   values_from = nitro_amon,
    #   id_cols = DATA_COLETA
    # ) %>% 
    ggplot(
      aes(x = DATA_COLETA,
          y = nitro_amon,
          # color = CODIGO
      ))+
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
    #       ymin = 13.3, 
    #       ymax = Inf,
    #       alpha= 0.005,
    #       fill= "#ac5079"),
    # show.legend = FALSE)+ #>pior classe
    # geom_rect(
    #   aes(xmin = periodo_inicial, 
    #       xmax = periodo_final,
  #       ymin= 3.7,
  #       ymax= 13.3,
  #       alpha= 0.005,
  #       fill= "#fcf7ab"),
  #    show.legend = FALSE)+ #classe 3
  # geom_rect(
  #   aes(xmin = periodo_inicial, 
  #       xmax = periodo_final,
  #       ymin= 0,
  #       ymax= 3.7,
  #       alpha= 0.005,
  #       fill= "blue"
  #         # "#8dcdeb"
  #         ),
  #    show.legend = FALSE)+ #classe 1
  annotate("rect",
           xmin= periodo_inicial,
           xmax= periodo_final,
           ymin=13.3,
           ymax=Inf,
           alpha= 0.7,
           fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin=3.7,
             ymax=13.3,
             alpha= 0.7,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin= periodo_inicial,
             xmax= periodo_final,
             ymin= -Inf,
             ymax=3.7,
             alpha= 0.7,
             fill="#8dcdeb")+ #classe 1
    geom_line(
      # aes(color = CODIGO),
      na.rm = TRUE)+
    geom_point(
      # aes(color = CODIGO),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
    # geom_smooth(
    #   # aes(color = CODIGO),
    #   method = "lm",
    #   # formula = y ~ poly(x, 2),
    #   # span = 0.2,
    #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
    #   aes(group = 1),
    #   alpha =.5,
    #   na.rm = TRUE,
    #   size = 0.3,
    #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~CODIGO,
    nrow = 4,
  )+
    theme_bw()
)
```


```{r Gráfico Nitrogênio total periodo1, warning = FALSE, message = FALSE}
(namon_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"1990" &
                               ANO_COLETA<="2000"),
                    aes(CODIGO,
                        nitro_total))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=3.7,
             ymax=13.3,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=0,
             ymax=3.7,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 1990-2000",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
                                 max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo2, warning = FALSE, message = FALSE}
(namon_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                    aes(CODIGO,
                        nitro_total))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2000-2010",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
                                 max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo3, warning = FALSE, message = FALSE}
(namon_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2010" &
                                 ANO_COLETA<="2020"),
                    aes(CODIGO,
                        nitro_total))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2010-2020",
        x="Estação",
        y="mg/L")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$nitro_total, na.rm = TRUE),
                                 max(plan_wide_19902020$nitro_total, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Namon 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
```

```{r Sumário Nitrogênio total, warning=FALSE, message = FALSE,}
(sum_namon_p1 <- plan_wide_19902020 %>%
   select(CODIGO, nitro_total, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(nitro_total, 
           na.rm = TRUE),
     q1 = 
       quantile(nitro_total, 0.25, 
                na.rm = TRUE),
     median = 
       median(nitro_total, 
              na.rm = TRUE),
     mean = 
       mean(nitro_total, 
            na.rm= TRUE),
     q3 = 
       quantile(nitro_total, 0.75, 
                na.rm = TRUE),
     max = 
       max(nitro_total, 
           na.rm = TRUE),
      n = 
       length(nitro_total)
   )
)

(sum_namon_p2 <- plan_wide_19902020 %>%
    select(CODIGO, nitro_total, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(nitro_total, 
            na.rm = TRUE),
      q1 = 
        quantile(nitro_total, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitro_total, 
               na.rm = TRUE),
      mean = 
        mean(nitro_total, 
             na.rm= TRUE),
      q3 = 
        quantile(nitro_total, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitro_total, 
            na.rm = TRUE))
)

(sum_namon_p3 <- plan_wide_19902020 %>%
    select(CODIGO, nitro_total, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(nitro_total, 
            na.rm = TRUE),
      q1 = 
        quantile(nitro_total, 0.25, 
                 na.rm = TRUE),
      median = 
        median(nitro_total, 
               na.rm = TRUE),
      mean = 
        mean(nitro_total, 
             na.rm= TRUE),
      q3 = 
        quantile(nitro_total, 0.75, 
                 na.rm = TRUE),
      max = 
        max(nitro_total, 
            na.rm = TRUE))
)
```

```{r Salvando namon, warning=FALSE, message = FALSE,}
ggsave("namon.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = namon,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p1.png",
       plot = namon_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p2.png",
       plot = namon_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p3.png",
       plot = namon_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Turbidez

```{r Gráfico Turbidez facetted, fig.cap="turbidez-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(turb <- ggplot(plan_wide_19902020,
                   aes(CODIGO,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   facet_wrap(~periodo)+
   labs(title = "Turbidez no período 1990-2020",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.05)),
                      n.breaks = 8,
                      limits = c(
                        # 1,
                        min(plan_wide_19902020$turbidez, na.rm = TRUE),
                        # 500
                        max(plan_wide_19902020$turbidez, na.rm = TRUE)
                      ),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez line, warning = FALSE, message = FALSE}
(turb_line <- plan_wide_19902020 %>%
  filter(ANO_COLETA > "1990" &
           ANO_COLETA <= "2020") %>%
  select(CODIGO, turbidez, DATA_COLETA, periodo) %>%
  group_by(CODIGO) %>%
  ggplot(
    aes(x = DATA_COLETA,
        y = turbidez,
        color = CODIGO
    ))+
    geom_line(
      # aes(color = CODIGO),
      na.rm = TRUE)+
    geom_point(
      # aes(color = CODIGO),
      na.rm = TRUE)+
    scale_x_date(
      limits = as.Date(c(
        "1990-01-01", 
        "2021-01-01"
        # NA #pode usar NA também
      )),
      expand = c(0.0, 0.0),
      date_breaks = "10 years",
      minor_breaks = "5 years",
      date_labels = "%Y",
    )+
  # geom_smooth(
  #   # aes(color = CODIGO),
  #   method = "lm",
  #   # formula = y ~ poly(x, 2),
  #   # span = 0.2,
  #   se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   aes(group = 1),
  #   alpha =.5,
  #   na.rm = TRUE,
  #   size = 0.3,
  #   # fullrange = TRUE,
  #   # show.legend = TRUE
  # )+
  # stat_smooth(
  #   geom = "smooth",
  #   # span = 0.2,
  #   se = FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
  #   # aes(group = 1),
  #   # alpha =.5,
  #   na.rm = TRUE,
  #   # size = 0.3,
  #   fullrange = TRUE,
  #   show.legend = TRUE
  # )+
  facet_wrap(
    ~CODIGO,
    nrow = 4,
  )+
  theme_bw()
)
```


```{r Gráfico Turbidez periodo1, warning = FALSE, message = FALSE}
(turb_p1 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"1990" &
                              ANO_COLETA<="2000"),
                   aes(CODIGO,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 1990-2000",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo2, warning = FALSE, message = FALSE}
(turb_p2 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2000" &
                              ANO_COLETA<="2010"),
                   aes(CODIGO,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2000-2010",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo3, warning = FALSE, message = FALSE}
(turb_p3 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2010" &
                              ANO_COLETA<="2020"),
                   aes(CODIGO,
                       turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2010-2020",
        x="Estação",
        y="UNT")+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico turb 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
```

```{r Sumário Turbidez, warning=FALSE, message = FALSE,}
(sum_turb_p1 <- plan_wide_19902020 %>%
   select(CODIGO, turbidez, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(turbidez, 
           na.rm = TRUE),
     q1 = 
       quantile(turbidez, 0.25, 
                na.rm = TRUE),
     median = 
       median(turbidez, 
              na.rm = TRUE),
     mean = 
       mean(turbidez, 
            na.rm= TRUE),
     q3 = 
       quantile(turbidez, 0.75, 
                na.rm = TRUE),
     max = 
       max(turbidez, 
           na.rm = TRUE))
)

(sum_turb_p2 <- plan_wide_19902020 %>%
    select(CODIGO, turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
)

(sum_turb_p3 <- plan_wide_19902020 %>%
    select(CODIGO, turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(turbidez, 
               na.rm = TRUE),
      mean = 
        mean(turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(turbidez, 
            na.rm = TRUE))
) 
```

```{r Salvando turb, warning=FALSE, message = FALSE,}
ggsave("turb.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = turb,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p1.png",
       plot = turb_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p2.png",
       plot = turb_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p3.png",
       plot = turb_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### pH

```{r Gráfico pH facetted, fig.cap="pH-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(pH <- ggplot(plan_wide_19902020,
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   facet_wrap(~periodo)+
   labs(title = "pH no período 1990-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo1, warning = FALSE, message = FALSE}
(pH_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"1990" &
                            ANO_COLETA<="2000"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 1990-2000",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo2, warning = FALSE, message = FALSE}
(pH_p2 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2000" &
                            ANO_COLETA<="2010"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo3, warning = FALSE, message = FALSE}
(pH_p3 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2010" &
                            ANO_COLETA<="2020"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2010-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
```

```{r Sumário pH, warning=FALSE, message = FALSE,}
(sum_pH_p1 <- plan_wide_19902020 %>%
   select(CODIGO, pH, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(pH, 
           na.rm = TRUE),
     q1 = 
       quantile(pH, 0.25, 
                na.rm = TRUE),
     median = 
       median(pH, 
              na.rm = TRUE),
     mean = 
       mean(pH, 
            na.rm= TRUE),
     q3 = 
       quantile(pH, 0.75, 
                na.rm = TRUE),
     max = 
       max(pH, 
           na.rm = TRUE))
)

(sum_pH_p2 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
) 

(sum_pH_p3 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
)
```

```{r Salvando pH, warning=FALSE, message = FALSE,}
ggsave("pH.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = pH,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p1.png",
       plot = pH_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p2.png",
       plot = pH_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p3.png",
       plot = pH_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Sólidos totais

```{r Gráfico SólTot facetted, fig.cap="sólidos-totais-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(SolTot <- ggplot(plan_wide_19902020,
                  aes(CODIGO,
                      solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   facet_wrap(~periodo)+
   labs(title = "Sólidos totais no período 1990-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo1, warning = FALSE, message = FALSE}
(SolTot_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"1990" &
                                ANO_COLETA<="2000"),
                     aes(CODIGO,
                         solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 1990-2000",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo2, warning = FALSE, message = FALSE}
(SolTot_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2000" &
                                ANO_COLETA<="2010"),
                     aes(CODIGO,
                         solidos_totais))+
   annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
       size = 1.2,
       alpha = .25,
       width = .07,
    )+
    scale_x_discrete(limits = c("87398500", 
                                "87398980", 
                                "87398900", 
                                "87398950", 
                                "87405500", 
                                "87406900", 
                                "87409900"),
                     labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
    )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
    theme_grafs()
)
```

```{r Gráfico SólTot periodo3, warning = FALSE, message = FALSE}
(SolTot_p3 <- ggplot(plan_wide_19902020 %>% 
                        filter(ANO_COLETA>"2010" &
                                  ANO_COLETA<="2020"),
                     aes(CODIGO,
                         solidos_totais))+
    annotate("rect",
            xmin = -Inf, xmax = Inf,
            ymin = 500, ymax = Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=500,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65))+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7)+
    labs(title = "Sólidos totais no período 2010-2020",
         x="Estação",
         y="")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$solidos_totais, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
```

```{r Sumário Sólidos Totais, warning=FALSE, message = FALSE,}
(sum_SolTot_p1 <- plan_wide_19902020 %>%
   select(CODIGO, solidos_totais, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(solidos_totais, 
           na.rm = TRUE),
     q1 = 
       quantile(solidos_totais, 0.25, 
                na.rm = TRUE),
     median = 
       median(solidos_totais, 
              na.rm = TRUE),
     mean = 
       mean(solidos_totais, 
            na.rm= TRUE),
     q3 = 
       quantile(solidos_totais, 0.75, 
                na.rm = TRUE),
     max = 
       max(solidos_totais, 
           na.rm = TRUE))
)

(sum_SolTot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, solidos_totais, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)

(sum_SolTot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, solidos_totais, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(solidos_totais, 
            na.rm = TRUE),
      q1 = 
        quantile(solidos_totais, 0.25, 
                 na.rm = TRUE),
      median = 
        median(solidos_totais, 
               na.rm = TRUE),
      mean = 
        mean(solidos_totais, 
             na.rm= TRUE),
      q3 = 
        quantile(solidos_totais, 0.75, 
                 na.rm = TRUE),
      max = 
        max(solidos_totais, 
            na.rm = TRUE))
)
```

```{r Salvando SolTot, warning=FALSE, message = FALSE,}
ggsave("SolTot.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = SolTot,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p1.png",
       plot = SolTot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p2.png",
       plot = SolTot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p3.png",
       plot = SolTot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### IQA

```{r Gráfico IQA facetted, fig.cap="iqa-gravataí no período 1990-2020", echo = FALSE, message=FALSE, warning=FALSE}
(iqa <-ggplot(plan_wide_19902020,
              aes(CODIGO,
                  IQA, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   facet_wrap(~periodo)+
   labs(title = "Variação do IQA no período 1990-2020",
        x="Estação",
        y="IQA")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
   # theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p1 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "1990" &
                            ANO_COLETA <= "2000"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
    annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA no período 1990-2000",
         x="Estação",
         y="")+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
   theme_grafs()+
   theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo2, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p2 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "2000" &
                            ANO_COLETA <= "2010"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2000-2010",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()+
   theme(axis.title.y = element_blank()
   )
)
```

```{r Gráfico IQA periodo3, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p3 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "2010" &
                            ANO_COLETA <= "2020"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=25,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=25,
            ymax=50,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=50,
            ymax=70,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=70,
            ymax=90,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=90,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2010-2020",
        x="Estação",
        y="")+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
    theme_grafs()+
    theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
```

```{r Sumário IQA, warning=FALSE, message = FALSE,}
(sum_IQA_p1 <- plan_wide_19902020 %>%
   select(CODIGO, IQA, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(IQA, 
           na.rm = TRUE),
     q1 = 
       quantile(IQA, 0.25, 
                na.rm = TRUE),
     median = 
       median(IQA, 
              na.rm = TRUE),
     mean = 
       mean(IQA, 
            na.rm= TRUE),
     q3 = 
       quantile(IQA, 0.75, 
                na.rm = TRUE),
     max = 
       max(IQA, 
           na.rm = TRUE),
     n = 
        length(IQA)
   )
)

(sum_IQA_p2 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE),
      n = 
        length(IQA)
      )
)

(sum_IQA_p3 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>%
    # ?as_factor(CODIGO) %>% 
    group_by(CODIGO) %>%
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE),
      n = 
        length(IQA),
      NAs = 
        sum(is.na(IQA))
      ) %>% 
  mutate(
    "%NA" = NAs/n*100
  )
)

```

```{r Salvando iqa, warning=FALSE, message = FALSE,}
ggsave("iqa.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = iqa,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p1.png",
       plot = iqa_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p2.png",
       plot = iqa_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p3.png",
       plot = iqa_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

## Testando coisas

```{r Testando coisas, include = FALSE, warning=FALSE, message = FALSE,}
# plan_wide_19902020 %>% 
#    select(CODIGO, oxigenio_dissolvido, ANO_COLETA) %>% 
#    ggplot(aes(ANO_COLETA, oxigenio_dissolvido, 
#       col = CODIGO))+
#    geom_line()+
#    facet_wrap(~ CODIGO, ncol = 7)

# df111 <- data.frame(x = c(1:100))
# glimpse(df111)
# df111$y <- 2 + 3 * df111$x + rnorm(100, sd = 40)
# 
# lm_eqn <- function(df111){
#     m <- lm(y ~ x, df111);
#     eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#          list(a = format(unname(coef(m)[1]), digits = 2),
#               b = format(unname(coef(m)[2]), digits = 2),
#              r2 = format(summary(m)$r.squared, digits = 3)))
#     as.character(as.expression(eq));
# } 
# p2 <- p111 +
#   geom_text(x = 25, y = 300,
#             label = lm_eqn(df111),
#             parse = TRUE)
# p2
# 
# 
# lm_eqc <- function(plan_wide_19902020){
#    m <- lm(oxigenio_dissolvido ~ CODIGO, plan_wide_19902020);
#    eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#                     list(a = format(unname(coef(m)[1]), digits = 2),
#                          b = format(unname(coef(m)[2]), digits = 2),
#                          r2 = format(summary(m)$r.squared, digits = 3)))
#    as.character(as.expression(eq));
# }
# 
# (od_p1 <-ggplot(plan_wide_19902020 %>%
#                    filter(ANO_COLETA>"1990" &
#                              ANO_COLETA<="2000"),
#                 aes(CODIGO,
#                     oxigenio_dissolvido))+
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=-Inf,
#                ymax=2,
#                alpha=1,
#                fill="#ac5079")+ #>pior classe
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=2,
#                ymax=4,
#                alpha=1,
#                fill="#eb5661")+ #classe 4
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=4,
#                ymax=5,
#                alpha=1,
#                fill="#fcf7ab")+ #classe 3
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=5,
#                ymax=6,
#                alpha=1,
#                fill="#70c18c")+ #classe 2
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=6,
#                ymax=Inf,
#                alpha=1,
#                fill="#8dcdeb")+ #classe 1
#       stat_boxplot(geom = 'errorbar',
#                    width=0.3,
#                    position = position_dodge(width = 0.65))+
#       geom_boxplot(fill='#F8F8FF',
#                    color="black",
#                    outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
#                    width= 0.7)+
#       labs(title = "Oxigênio Dissolvido no período 1990-2000",
#            x="Estação",
#            y="mg/L")+
#       # geom_jitter(width = .05,
#       #             alpha=.2,
#       #             size=1.5,
#       #             color="black")+
#       scale_y_continuous(expand = expansion(mult = c(0,0)),
#                          n.breaks = 11,
#                          limits = c(-1,21))+
#       scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
#       geom_smooth(method = "lm",
#                   se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                   aes(group=1),
#                   alpha=.5,
#                   na.rm = TRUE,
#                   size = 1)+
#       # annotate(geom_text(aes(x = "87405500", y = 15),
#       #                    label = lm_eqc(plan_wide_19902020),
#       #                    parse = TRUE,
#       #                    inherit.aes = TRUE,
#       #                    check_overlap = TRUE))+
#       #  geom_line(
#       #     aes(color="red"),
#       #     alpha=.0,
#       # )+
#       # scale_color_manual("Legenda",
#       #                    guide="legend",
#       #                    values = c("Classe 1"="#8dcdeb",
#       #                               "Classe 2"="#70c18c",
#       #                               "Classe 3"="#fcf7ab",
#       #                               "Classe 4"="#eb5661",
#       #                               "Pior Classe"="#ac5079"))+
#    # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#    #                                               lwd=c(2,2,2,2,2),
#    #                                               shape=c(NA,NA,NA,NA,NA),
#    #                                               alpha=1)))+
#       theme(legend.position = "bottom")+
#       theme_classic())

# list1111 <- sessionInfo()
# list1111$loadedOnly

# install.packages("ggpmisc")
# library(ggpmisc)

# summary(lm(plan_wide_19902020$CODIGO~plan_wide_19902020$DBO))
# 
# 
# p <- ggplot(data, aes(y=number, x=pod)) +
#   geom_boxplot()
# print(p)

# install.packages("GGally")


# fit = lm(plan_wide_19902020$oxigenio_dissolvido~ plan_wide_19902020$CODIGO)
# summary(fit)
# summary.lm(fit)
# 
# pacman::p_load(esquisse)

# sumario <- function(x, y){
#   x %>% 
#     group_by(CODIGO) %>%
#     summarise(
#       list(min= ~min(y, na.rm = TRUE), 
#            Q1= ~quantile(y, probs = 0.25),
#            median= ~median(y, na.rm = TRUE), 
#            Q3= ~quantile(y, probs = 0.75),
#            max= ~max(y, na.rm = TRUE)),
#       .groups = "drop"
#       )
# }
```

### Correlação

```{r Correlação, fig.cap="correlação-parametros-qualidade-agua-gravataí no período 1990-2020", time_it = TRUE, warning=FALSE, message = FALSE,}
parametros_IQA %>% 
  select(
    -CODIGO,
    -nitro_total) %>% 
  # group_by(CODIGO) %>% 
  rename(
    CE = Condutividade,
    OD = oxigenio_dissolvido,
    ST = solidos_totais,
    Turb = turbidez,
    Temp = temp_agua,
    Ptot = fosforo_total,
    NAmon = nitro_amon,
    NTK = nitro_kjeldahl
  ) %>% 
  ggcorr(
    method = "complete.obs",
    # "pearson",
    # "pairwise",
    name = "Correlação",
    label = TRUE,
    label_alpha = TRUE,
    digits = 3,
    low = "#3B9AB2",
    mid = "#EEEEEE",
    high = "#F21A00",
    # palette = "RdYlBu",
    layout.exp = 0,
    legend.position = "left",
    label_round = 3,
    # legend.size = 18,
    geom = "tile",
    nbreaks = 10,
  )+
  labs(title = "Correlação entre parâmetros físico-químicos na\nBacia Hidrográfica do rio Gravataí no período 1990-2020")+
  theme_linedraw()+
  theme(
    legend.position = c(0.15, 0.6),
    legend.title = element_text(size = 16),
    legend.text = element_text(size = 14),
    # legend.spacing = unit(element_text(),
                          # units = 5)
    plot.title = element_text(hjust = 0.5,
                              size = 16)
  )

# Gráfico das correlações entre todos os parâmetros com significância
correl_IQA <- parametros_IQA %>%
  select(-CODIGO) %>%
  ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
          axisLabels = "show")

correlacao_pIQA <- parametros_IQA %>% 
  group_by(CODIGO) %>% 
  correlation::correlation()

correlacao_pIQA %>% 
  # glimpse()
  filter(
    p < 0.001
  ) %>% 
  t() %>% 
  summary()

parametros_IQA %>% 
  # group_by(CODIGO) %>% 
  select(
    nitro_kjeldahl, Condutividade
  ) %>% 
  # correlation::cor_test() %>% 
  plot()

str(
  plot(
    correlation::cor_test(
      parametros_IQA,
      "nitro_kjeldahl",
      "Condutividade"
    )
  )
)
```

### Condutividade elétrica
```{r Gráfico cond_elet facetted, fig.cap="condutividade-eletrica-gravataí no período 1990-2020", warning = FALSE, message = FALSE}
(cond_elet <- ggplot(plan_wide_19902020,
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   facet_wrap(~periodo)+
      labs(title = "Condutividade elétrica no período 1990-2020",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet periodo1, warning = FALSE, message = FALSE}
(cond_elet_p1 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2000" &
                                   ANO_COLETA<="2010"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
      labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet periodo2, warning = FALSE, message = FALSE}
(cond_elet_p2 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2000" &
                                   ANO_COLETA<="2010"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 2000-2010",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet periodo3, warning = FALSE, message = FALSE}
(cond_elet_p3 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2010" &
                                   ANO_COLETA<="2020"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 2010-2020",
        x="Estação",
        y="µmhos/cm")+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico cond_elet 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
```

```{r Sumário cond_elet, warning=FALSE, message = FALSE}
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
   select(CODIGO, Condutividade, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(Condutividade, 
           na.rm = TRUE),
     q1 = 
       quantile(Condutividade, 0.25, 
                na.rm = TRUE),
     median = 
       median(Condutividade, 
              na.rm = TRUE),
     mean = 
       mean(Condutividade, 
            na.rm= TRUE),
     q3 = 
       quantile(Condutividade, 0.75, 
                na.rm = TRUE),
     max = 
       max(Condutividade, 
           na.rm = TRUE))
)

(sum_cond_elet_p2 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE))
)

(sum_cond_elet_p3 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE),
      n = 
        length(Condutividade))
)

# plan_wide_19902020 %>% 
#    select(CODIGO, IQA) %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#       min = 
#          min(IQA, 
#              na.rm = TRUE),
#       q1 = 
#          quantile(IQA, 0.25, 
#                   na.rm = TRUE),
#       median = 
#          median(IQA, 
#                 na.rm = TRUE),
#       mean = 
#          mean(IQA, 
#               na.rm= TRUE),
#       q3 = 
#          quantile(IQA, 0.75, 
#                   na.rm = TRUE),
#       max = 
#          max(IQA, 
#              na.rm = TRUE))
```

```{r Salvando cond_elet, warning=FALSE, message = FALSE}
ggsave("cond_elet.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = cond_elet,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p1.png",
       plot = cond_elet_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p2.png",
       plot = cond_elet_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p3.png",
       plot = cond_elet_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

```


## Textando o texto

* § falar do comportamento geral dos dados
* 2º § - xº § -> abordar os principais parâmetros que estão sendo impactados, detalhando, nas estações mais relevantes, como ficaram os quartis/mediana etc.



`r sum_od_p1$PM1[1]`

Os resultados apontam que para o parâmetro OD









